rewire.it
Task

GlycanML taxonomy prediction

This GlycanML task evaluates taxonomy classification using glycan representations.

Sources (3)GlycanML/GlycanML official source; GlycanML/GlycanML configs/single_task/BERT/species_BERT.yaml; GlycanML/GlycanML module/custom_datasets/glycan_classification.py · Pinned README: Introduction; Experiment configurations; leaderboard; pinned configs/single_task/BERT/species_BERT.yaml, module/custom_datasets/glycan_classification.py (task metric, dataset class and split methods)

190 evaluations · 190 metric rows

At a glance

Inputs, training, access and other details

Explanatory profile: source reviewed · Automated source review, 2026-09-16. Review applies to the cited claims; unresolved fields are listed below. Numerical results retain their own review status.

Data, procedure and scoring
PropertyDescription and evidence
DatasetsHierarchical taxonomy labels span species, genus, family, order, class, phylum, kingdom and domain; the checked single-task configuration targets species.
Sources (3)GlycanML/GlycanML official source; GlycanML/GlycanML configs/single_task/BERT/species_BERT.yaml; GlycanML/GlycanML module/custom_datasets/glycan_classification.py · Pinned README: Introduction; Experiment configurations; leaderboard; pinned configs/single_task/BERT/species_BERT.yaml, module/custom_datasets/glycan_classification.py (task metric, dataset class and split methods)
SplitsGlycans are represented by motif frequencies and clustered; motif groups are allocated to training, validation and test in an 8:1:1 grouping scheme. Table 1 preserves the resulting dataset-specific counts.
Sourcesglycanml primary benchmark evidence · Sections 3.1–3.3; Table 1
MetricsAccuracy and Matthews correlation coefficient in the checked species-classification configuration.
Sources (3)GlycanML/GlycanML official source; GlycanML/GlycanML configs/single_task/BERT/species_BERT.yaml; GlycanML/GlycanML module/custom_datasets/glycan_classification.py · Pinned README: Introduction; Experiment configurations; leaderboard; pinned configs/single_task/BERT/species_BERT.yaml, module/custom_datasets/glycan_classification.py (task metric, dataset class and split methods)
BaselinesSequence-model CNN, ResNet, LSTM and BERT configurations; graph-model GCN, RGCN, GAT, GIN, CompGCN and MPNN configurations.
Sources (3)GlycanML/GlycanML official source; GlycanML/GlycanML configs/single_task/BERT/species_BERT.yaml; GlycanML/GlycanML module/custom_datasets/glycan_classification.py · Pinned README: Introduction; Experiment configurations; leaderboard; pinned configs/single_task/BERT/species_BERT.yaml, module/custom_datasets/glycan_classification.py (task metric, dataset class and split methods)
Leakage controlsMotif-based cluster separation tests transfer to structurally different glycans. This is a glycan-structure control, not a claim that all organisms or source studies are held out.
Sourcesglycanml primary benchmark evidence · Sections 3.1–3.3; Table 1
UncertaintyEvery experiment uses seeds 0, 1 and 2; reported summaries are the mean and standard deviation over those three runs.
Sourcesglycanml primary benchmark evidence · Sections 3.1–3.4 and 5.1; Tables 1 and 3
Entity typeConstituent benchmark task: GlycanML taxonomy prediction
Sources (3)GlycanML/GlycanML official source; GlycanML/GlycanML configs/single_task/BERT/species_BERT.yaml; GlycanML/GlycanML module/custom_datasets/glycan_classification.py · Pinned README: Introduction; Experiment configurations; leaderboard; pinned configs/single_task/BERT/species_BERT.yaml, module/custom_datasets/glycan_classification.py (task metric, dataset class and split methods)
OrganismsTaxonomy tasks explicitly predict organism categories; species scope depends on the constituent dataset.
Sources (3)GlycanML/GlycanML official source; GlycanML/GlycanML configs/single_task/BERT/species_BERT.yaml; GlycanML/GlycanML module/custom_datasets/glycan_classification.py · Pinned README: Introduction; Experiment configurations; leaderboard; pinned configs/single_task/BERT/species_BERT.yaml, module/custom_datasets/glycan_classification.py (task metric, dataset class and split methods)
AssaysTaxonomy, immunogenicity, glycosylation-type and protein–glycan interaction annotations.
Sources (3)GlycanML/GlycanML official source; GlycanML/GlycanML configs/single_task/BERT/species_BERT.yaml; GlycanML/GlycanML module/custom_datasets/glycan_classification.py · Pinned README: Introduction; Experiment configurations; leaderboard; pinned configs/single_task/BERT/species_BERT.yaml, module/custom_datasets/glycan_classification.py (task metric, dataset class and split methods)
Allowed inputsGlycan sequence or graph representation with an organism-taxonomy target.
Sources (3)GlycanML/GlycanML official source; GlycanML/GlycanML configs/single_task/BERT/species_BERT.yaml; GlycanML/GlycanML module/custom_datasets/glycan_classification.py · Pinned README: Introduction; Experiment configurations; leaderboard; pinned configs/single_task/BERT/species_BERT.yaml, module/custom_datasets/glycan_classification.py (task metric, dataset class and split methods)
AdaptationSeparate single-task and multi-task training configurations are supplied.
Sources (3)GlycanML/GlycanML official source; GlycanML/GlycanML configs/single_task/BERT/species_BERT.yaml; GlycanML/GlycanML module/custom_datasets/glycan_classification.py · Pinned README: Introduction; Experiment configurations; leaderboard; pinned configs/single_task/BERT/species_BERT.yaml, module/custom_datasets/glycan_classification.py (task metric, dataset class and split methods)

How it works

How it worksEvaluation procedure
Evaluation procedure1. Allowed inputs: Glycan sequence or graph representation with an organism-taxonomy target.. Then: 2. Splits: Glycans are represented by motif frequencies and clustered; motif groups are allocated to training, validation and test in an 8:1:1 grouping scheme. Table 1 preserves the resulting dataset-specific counts.. Then: 3. Metrics: Accuracy and Matthews correlation coefficient in the checked species-classification configuration.Evaluation procedure1. Allowed inputs: Glycan sequence or graph representation with an organism-taxonomy target.. Then: 2. Splits: Glycans are represented by motif frequencies and clustered; motif groups are allocated to training, validation and test in an 8:1:1 grouping scheme. Table 1 preserves the resulting dataset-specific counts.. Then: 3. Metrics: Accuracy and Matthews correlation coefficient in the checked species-classification configuration.Evaluation procedure1. Allowed inputs: Glycan sequence or graph representation with an organism-taxonomy target.. Then: 2. Splits: Glycans are represented by motif frequencies and clustered; motif groups are allocated to training, validation and test in an 8:1:1 grouping scheme. Table 1 preserves the resulting dataset-specific counts.. Then: 3. Metrics: Accuracy and Matthews correlation coefficient in the checked species-classification configuration.

Conceptual procedure. Task variants and protocol versions retain their separate scoring conditions.

Sources (4)GlycanML/GlycanML official source; GlycanML/GlycanML configs/single_task/BERT/species_BERT.yaml; GlycanML/GlycanML module/custom_datasets/glycan_classification.py; glycanml primary benchmark evidence · Pinned README: Introduction; Experiment configurations; leaderboard; pinned configs/single_task/BERT/species_BERT.yaml, module/custom_datasets/glycan_classification.py (task metric, dataset class and split methods); Sections 3.1–3.3; Table 1
Evaluation methodology

Hierarchical taxonomy labels span species, genus, family, order, class, phylum, kingdom and domain; the checked single-task configuration targets species. Dataset loader retains the train/validation/test assignments supplied in the downloaded CSV; this inspection does not establish how those original assignments were constructed. Accuracy and Matthews correlation coefficient in the checked species-classification configuration. Sequence-model CNN, ResNet, LSTM and BERT configurations; graph-model GCN, RGCN, GAT, GIN, CompGCN and MPNN configurations.

Sources (3)GlycanML/GlycanML official source; GlycanML/GlycanML configs/single_task/BERT/species_BERT.yaml; GlycanML/GlycanML module/custom_datasets/glycan_classification.py · Pinned README: Introduction; Experiment configurations; leaderboard; pinned configs/single_task/BERT/species_BERT.yaml, module/custom_datasets/glycan_classification.py (task metric, dataset class and split methods)

Evaluation design

Benchmarks bring together tasks and protocols. A task describes the biological question; a protocol defines a particular test.

Benchmarks

Protocols

These source-backed links do not make different protocols or scores interchangeable.

Published comparisons

Explore the results reported under one evaluation protocol. Each figure keeps its source, dataset and metric together; it is not a ranking across studies.

GlycanML Domain · Table 3, p. 8

accuracy (percent) · Higher values are better for this metric.

Author-reported single-task models on the same held-out task; not a cross-paper ranking. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.

Evaluation protocol · SugarBase taxonomy · Domain

  1. Shallow CNN · Configuration · Author-reported evaluation93.76(0.66)
  2. ResNet · Configuration · Author-reported evaluation93.29(1.46)
  3. LSTM · Configuration · Author-reported evaluation92.78(1.16)
  4. Transformer · Configuration · Author-reported evaluation91.98(0.27)
  5. GCN · Configuration · Author-reported evaluation94.38(0.51)
  6. GAT · Configuration · Author-reported evaluation94.27(0.41)
  7. GIN · Configuration · Author-reported evaluation94.41(0.17)
  8. MPNN · Configuration · Author-reported evaluation93.83(0.17)
  9. RGCN · Configuration · Author-reported evaluation94.78(0.11)
  10. CompGCN · Configuration · Author-reported evaluation93.94(0.41)

Source order is preserved. Plotted marks show point estimates; uncertainty, where reported, is retained in the printed values and table. Differences do not establish statistical significance.

GlycanML: A Multi-Task and Multi-Structure Benchmark for Glycan Machine Learning · Table 3, p. 8, Domain
Values, uncertainty and evidence
accuracy: original source values
Tested entityPrinted valueUncertaintyEvidence
Shallow CNN · Configuration93.76(0.66) percenttype: standard_deviation; value: 0.66; n: 3; unit: same_as_metricAuthor-reported evaluation · source checkedGlycanML: A Multi-Task and Multi-Structure Benchmark for Glycan Machine Learning · Table 3, row Shallow CNN, column Domain
ResNet · Configuration93.29(1.46) percenttype: standard_deviation; value: 1.46; n: 3; unit: same_as_metricAuthor-reported evaluation · source checkedGlycanML: A Multi-Task and Multi-Structure Benchmark for Glycan Machine Learning · Table 3, row ResNet, column Domain
LSTM · Configuration92.78(1.16) percenttype: standard_deviation; value: 1.16; n: 3; unit: same_as_metricAuthor-reported evaluation · source checkedGlycanML: A Multi-Task and Multi-Structure Benchmark for Glycan Machine Learning · Table 3, row LSTM, column Domain
Transformer · Configuration91.98(0.27) percenttype: standard_deviation; value: 0.27; n: 3; unit: same_as_metricAuthor-reported evaluation · source checkedGlycanML: A Multi-Task and Multi-Structure Benchmark for Glycan Machine Learning · Table 3, row Transformer, column Domain
GCN · Configuration94.38(0.51) percenttype: standard_deviation; value: 0.51; n: 3; unit: same_as_metricAuthor-reported evaluation · source checkedGlycanML: A Multi-Task and Multi-Structure Benchmark for Glycan Machine Learning · Table 3, row GCN, column Domain
GAT · Configuration94.27(0.41) percenttype: standard_deviation; value: 0.41; n: 3; unit: same_as_metricAuthor-reported evaluation · source checkedGlycanML: A Multi-Task and Multi-Structure Benchmark for Glycan Machine Learning · Table 3, row GAT, column Domain
GIN · Configuration94.41(0.17) percenttype: standard_deviation; value: 0.17; n: 3; unit: same_as_metricAuthor-reported evaluation · source checkedGlycanML: A Multi-Task and Multi-Structure Benchmark for Glycan Machine Learning · Table 3, row GIN, column Domain
MPNN · Configuration93.83(0.17) percenttype: standard_deviation; value: 0.17; n: 3; unit: same_as_metricAuthor-reported evaluation · source checkedGlycanML: A Multi-Task and Multi-Structure Benchmark for Glycan Machine Learning · Table 3, row MPNN, column Domain
RGCN · Configuration94.78(0.11) percenttype: standard_deviation; value: 0.11; n: 3; unit: same_as_metricAuthor-reported evaluation · source checkedGlycanML: A Multi-Task and Multi-Structure Benchmark for Glycan Machine Learning · Table 3, row RGCN, column Domain
CompGCN · Configuration93.94(0.41) percenttype: standard_deviation; value: 0.41; n: 3; unit: same_as_metricAuthor-reported evaluation · source checkedGlycanML: A Multi-Task and Multi-Structure Benchmark for Glycan Machine Learning · Table 3, row CompGCN, column Domain
Scope and limitations
  • Taxonomic levels are distinct tasks; do not combine raw accuracy across levels.
  • Protein–glycan scores evaluate combined glycan encoders plus ESM-1b/MLP, not an ESM-1b standalone checkpoint.
  • Cluster manifests and trained checkpoint hashes are not enumerated in the paper table.

Source transcription and grouping reviewed by automated source review on 2026-09-17. These experiments were not independently reproduced by rewire.

Tested entities and results

Release 2026-09-17-d277315f7d76 · 190 evaluations · 190 metric rows. Different protocols are not a single leaderboard.

Results grouped by the exact reported evaluation
Metric and findingCoverage and uncertaintyEvidence
Shallow CNN / UW: SugarBase taxonomy · Mean Acc
Configuration: Shallow CNN / UWProtocol: SugarBase taxonomy · Mean Acc (GlycanML taxonomy prediction)Dataset: SugarBase taxonomy · Mean Acc

Fixed backbone; compare adaptation strategies, not standalone foundation models. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.

Author-reported evaluation · Evaluation metadata: needs review

62.87(0.87)% mean accuracy across eight taxonomy tasks

Unit: percent · Direction: higher

Uncertainty: type: standard deviation; value: 0.87; n: 3; unit: same as metric

Scored: Not reported · Eligible: Not reported

source checkedGlycanML: A Multi-Task and Multi-Structure Benchmark for Glycan Machine Learning · Table 4, Shallow CNN, UW, column Mean Acc (%)

Source checking is not independent reproduction.

LSTM: SugarBase taxonomy · Genus
Configuration: LSTMProtocol: SugarBase taxonomy · Genus (GlycanML taxonomy prediction)Dataset: SugarBase taxonomy · Genus

Author-reported single-task models on the same held-out task; not a cross-paper ranking. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.

Author-reported evaluation · Evaluation metadata: needs review

28.87(2.77)% accuracy

Unit: percent · Direction: higher

Uncertainty: type: standard deviation; value: 2.77; n: 3; unit: same as metric

Scored: Not reported · Eligible: Not reported

source checkedGlycanML: A Multi-Task and Multi-Structure Benchmark for Glycan Machine Learning · Table 3, row LSTM, column Genus

Source checking is not independent reproduction.

Shallow CNN: SugarBase taxonomy · Domain
Configuration: Shallow CNNProtocol: SugarBase taxonomy · Domain (GlycanML taxonomy prediction)Dataset: SugarBase taxonomy · Domain

Author-reported single-task models on the same held-out task; not a cross-paper ranking. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.

Author-reported evaluation · Evaluation metadata: needs review

93.76(0.66)% accuracy

Unit: percent · Direction: higher

Uncertainty: type: standard deviation; value: 0.66; n: 3; unit: same as metric

Scored: Not reported · Eligible: Not reported

source checkedGlycanML: A Multi-Task and Multi-Structure Benchmark for Glycan Machine Learning · Table 3, row Shallow CNN, column Domain

Source checking is not independent reproduction.

RGCN / UW: SugarBase taxonomy · Phylum
Configuration: RGCN / UWProtocol: SugarBase taxonomy · Phylum (GlycanML taxonomy prediction)Dataset: SugarBase taxonomy · Phylum

Fixed backbone; compare adaptation strategies, not standalone foundation models. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.

Author-reported evaluation · Evaluation metadata: needs review

85.60(0.41)% accuracy

Unit: percent · Direction: higher

Uncertainty: type: standard deviation; value: 0.41; n: 3; unit: same as metric

Scored: Not reported · Eligible: Not reported

source checkedGlycanML: A Multi-Task and Multi-Structure Benchmark for Glycan Machine Learning · Table 4, RGCN, UW, column Phylum

Source checking is not independent reproduction.

Shallow CNN: SugarBase taxonomy · Kingdom
Configuration: Shallow CNNProtocol: SugarBase taxonomy · Kingdom (GlycanML taxonomy prediction)Dataset: SugarBase taxonomy · Kingdom

Author-reported single-task models on the same held-out task; not a cross-paper ranking. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.

Author-reported evaluation · Evaluation metadata: needs review

91.66(2.29)% accuracy

Unit: percent · Direction: higher

Uncertainty: type: standard deviation; value: 2.29; n: 3; unit: same as metric

Scored: Not reported · Eligible: Not reported

source checkedGlycanML: A Multi-Task and Multi-Structure Benchmark for Glycan Machine Learning · Table 3, row Shallow CNN, column Kingdom

Source checking is not independent reproduction.

Shallow CNN: SugarBase taxonomy · Family
Configuration: Shallow CNNProtocol: SugarBase taxonomy · Family (GlycanML taxonomy prediction)Dataset: SugarBase taxonomy · Family

Author-reported single-task models on the same held-out task; not a cross-paper ranking. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.

Author-reported evaluation · Evaluation metadata: needs review

45.27(1.52)% accuracy

Unit: percent · Direction: higher

Uncertainty: type: standard deviation; value: 1.52; n: 3; unit: same as metric

Scored: Not reported · Eligible: Not reported

source checkedGlycanML: A Multi-Task and Multi-Structure Benchmark for Glycan Machine Learning · Table 3, row Shallow CNN, column Family

Source checking is not independent reproduction.

ResNet: SugarBase taxonomy · Domain
Configuration: ResNetProtocol: SugarBase taxonomy · Domain (GlycanML taxonomy prediction)Dataset: SugarBase taxonomy · Domain

Author-reported single-task models on the same held-out task; not a cross-paper ranking. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.

Author-reported evaluation · Evaluation metadata: needs review

93.29(1.46)% accuracy

Unit: percent · Direction: higher

Uncertainty: type: standard deviation; value: 1.46; n: 3; unit: same as metric

Scored: Not reported · Eligible: Not reported

source checkedGlycanML: A Multi-Task and Multi-Structure Benchmark for Glycan Machine Learning · Table 3, row ResNet, column Domain

Source checking is not independent reproduction.

Shallow CNN / N-MTL: SugarBase taxonomy · Class
Configuration: Shallow CNN / N-MTLProtocol: SugarBase taxonomy · Class (GlycanML taxonomy prediction)Dataset: SugarBase taxonomy · Class

Fixed backbone; compare adaptation strategies, not standalone foundation models. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.

Author-reported evaluation · Evaluation metadata: needs review

70.58(0.44)% accuracy

Unit: percent · Direction: higher

Uncertainty: type: standard deviation; value: 0.44; n: 3; unit: same as metric

Scored: Not reported · Eligible: Not reported

source checkedGlycanML: A Multi-Task and Multi-Structure Benchmark for Glycan Machine Learning · Table 4, Shallow CNN, N-MTL, column Class

Source checking is not independent reproduction.

GAT: SugarBase taxonomy · Order
Configuration: GATProtocol: SugarBase taxonomy · Order (GlycanML taxonomy prediction)Dataset: SugarBase taxonomy · Order

Author-reported single-task models on the same held-out task; not a cross-paper ranking. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.

Author-reported evaluation · Evaluation metadata: needs review

40.77(2.16)% accuracy

Unit: percent · Direction: higher

Uncertainty: type: standard deviation; value: 2.16; n: 3; unit: same as metric

Scored: Not reported · Eligible: Not reported

source checkedGlycanML: A Multi-Task and Multi-Structure Benchmark for Glycan Machine Learning · Table 3, row GAT, column Order

Source checking is not independent reproduction.

Shallow CNN / DWA: SugarBase taxonomy · Family
Configuration: Shallow CNN / DWAProtocol: SugarBase taxonomy · Family (GlycanML taxonomy prediction)Dataset: SugarBase taxonomy · Family

Fixed backbone; compare adaptation strategies, not standalone foundation models. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.

Author-reported evaluation · Evaluation metadata: needs review

41.57(2.17)% accuracy

Unit: percent · Direction: higher

Uncertainty: type: standard deviation; value: 2.17; n: 3; unit: same as metric

Scored: Not reported · Eligible: Not reported

source checkedGlycanML: A Multi-Task and Multi-Structure Benchmark for Glycan Machine Learning · Table 4, Shallow CNN, DWA, column Family

Source checking is not independent reproduction.

Shallow CNN / GN: SugarBase taxonomy · Domain
Configuration: Shallow CNN / GNProtocol: SugarBase taxonomy · Domain (GlycanML taxonomy prediction)Dataset: SugarBase taxonomy · Domain

Fixed backbone; compare adaptation strategies, not standalone foundation models. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.

Author-reported evaluation · Evaluation metadata: needs review

92.96(0.49)% accuracy

Unit: percent · Direction: higher

Uncertainty: type: standard deviation; value: 0.49; n: 3; unit: same as metric

Scored: Not reported · Eligible: Not reported

source checkedGlycanML: A Multi-Task and Multi-Structure Benchmark for Glycan Machine Learning · Table 4, Shallow CNN, GN, column Domain

Source checking is not independent reproduction.

RGCN / UW: SugarBase taxonomy · Kingdom
Configuration: RGCN / UWProtocol: SugarBase taxonomy · Kingdom (GlycanML taxonomy prediction)Dataset: SugarBase taxonomy · Kingdom

Fixed backbone; compare adaptation strategies, not standalone foundation models. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.

Author-reported evaluation · Evaluation metadata: needs review

91.77(0.23)% accuracy

Unit: percent · Direction: higher

Uncertainty: type: standard deviation; value: 0.23; n: 3; unit: same as metric

Scored: Not reported · Eligible: Not reported

source checkedGlycanML: A Multi-Task and Multi-Structure Benchmark for Glycan Machine Learning · Table 4, RGCN, UW, column Kingdom

Source checking is not independent reproduction.

CompGCN: SugarBase taxonomy · Family
Configuration: CompGCNProtocol: SugarBase taxonomy · Family (GlycanML taxonomy prediction)Dataset: SugarBase taxonomy · Family

Author-reported single-task models on the same held-out task; not a cross-paper ranking. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.

Author-reported evaluation · Evaluation metadata: needs review

44.03(1.51)% accuracy

Unit: percent · Direction: higher

Uncertainty: type: standard deviation; value: 1.51; n: 3; unit: same as metric

Scored: Not reported · Eligible: Not reported

source checkedGlycanML: A Multi-Task and Multi-Structure Benchmark for Glycan Machine Learning · Table 3, row CompGCN, column Family

Source checking is not independent reproduction.

RGCN / N-MTL: SugarBase taxonomy · Mean Acc
Configuration: RGCN / N-MTLProtocol: SugarBase taxonomy · Mean Acc (GlycanML taxonomy prediction)Dataset: SugarBase taxonomy · Mean Acc

Fixed backbone; compare adaptation strategies, not standalone foundation models. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.

Author-reported evaluation · Evaluation metadata: needs review

64.45(0.40)% mean accuracy across eight taxonomy tasks

Unit: percent · Direction: higher

Uncertainty: type: standard deviation; value: 0.4; n: 3; unit: same as metric

Scored: Not reported · Eligible: Not reported

source checkedGlycanML: A Multi-Task and Multi-Structure Benchmark for Glycan Machine Learning · Table 4, RGCN, N-MTL, column Mean Acc (%)

Source checking is not independent reproduction.

RGCN / N-MTL: SugarBase taxonomy · Genus
Configuration: RGCN / N-MTLProtocol: SugarBase taxonomy · Genus (GlycanML taxonomy prediction)Dataset: SugarBase taxonomy · Genus

Fixed backbone; compare adaptation strategies, not standalone foundation models. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.

Author-reported evaluation · Evaluation metadata: needs review

40.66(1.36)% accuracy

Unit: percent · Direction: higher

Uncertainty: type: standard deviation; value: 1.36; n: 3; unit: same as metric

Scored: Not reported · Eligible: Not reported

source checkedGlycanML: A Multi-Task and Multi-Structure Benchmark for Glycan Machine Learning · Table 4, RGCN, N-MTL, column Genus

Source checking is not independent reproduction.

Shallow CNN / GN: SugarBase taxonomy · Mean Acc
Configuration: Shallow CNN / GNProtocol: SugarBase taxonomy · Mean Acc (GlycanML taxonomy prediction)Dataset: SugarBase taxonomy · Mean Acc

Fixed backbone; compare adaptation strategies, not standalone foundation models. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.

Author-reported evaluation · Evaluation metadata: needs review

63.13(0.25)% mean accuracy across eight taxonomy tasks

Unit: percent · Direction: higher

Uncertainty: type: standard deviation; value: 0.25; n: 3; unit: same as metric

Scored: Not reported · Eligible: Not reported

source checkedGlycanML: A Multi-Task and Multi-Structure Benchmark for Glycan Machine Learning · Table 4, Shallow CNN, GN, column Mean Acc (%)

Source checking is not independent reproduction.

RGCN / TS: SugarBase taxonomy · Mean Acc
Configuration: RGCN / TSProtocol: SugarBase taxonomy · Mean Acc (GlycanML taxonomy prediction)Dataset: SugarBase taxonomy · Mean Acc

Fixed backbone; compare adaptation strategies, not standalone foundation models. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.

Author-reported evaluation · Evaluation metadata: needs review

66.68(0.23)% mean accuracy across eight taxonomy tasks

Unit: percent · Direction: higher

Uncertainty: type: standard deviation; value: 0.23; n: 3; unit: same as metric

Scored: Not reported · Eligible: Not reported

source checkedGlycanML: A Multi-Task and Multi-Structure Benchmark for Glycan Machine Learning · Table 4, RGCN, TS, column Mean Acc (%)

Source checking is not independent reproduction.

RGCN / GN: SugarBase taxonomy · Mean Acc
Configuration: RGCN / GNProtocol: SugarBase taxonomy · Mean Acc (GlycanML taxonomy prediction)Dataset: SugarBase taxonomy · Mean Acc

Fixed backbone; compare adaptation strategies, not standalone foundation models. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.

Author-reported evaluation · Evaluation metadata: needs review

63.83(0.65)% mean accuracy across eight taxonomy tasks

Unit: percent · Direction: higher

Uncertainty: type: standard deviation; value: 0.65; n: 3; unit: same as metric

Scored: Not reported · Eligible: Not reported

source checkedGlycanML: A Multi-Task and Multi-Structure Benchmark for Glycan Machine Learning · Table 4, RGCN, GN, column Mean Acc (%)

Source checking is not independent reproduction.

Transformer: SugarBase taxonomy · Species
Configuration: TransformerProtocol: SugarBase taxonomy · Species (GlycanML taxonomy prediction)Dataset: SugarBase taxonomy · Species

Author-reported single-task models on the same held-out task; not a cross-paper ranking. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.

Author-reported evaluation · Evaluation metadata: needs review

27.49(1.20)% accuracy

Unit: percent · Direction: higher

Uncertainty: type: standard deviation; value: 1.2; n: 3; unit: same as metric

Scored: Not reported · Eligible: Not reported

source checkedGlycanML: A Multi-Task and Multi-Structure Benchmark for Glycan Machine Learning · Table 3, row Transformer, column Species

Source checking is not independent reproduction.

Shallow CNN / TS: SugarBase taxonomy · Kingdom
Configuration: Shallow CNN / TSProtocol: SugarBase taxonomy · Kingdom (GlycanML taxonomy prediction)Dataset: SugarBase taxonomy · Kingdom

Fixed backbone; compare adaptation strategies, not standalone foundation models. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.

Author-reported evaluation · Evaluation metadata: needs review

91.55(1.03)% accuracy

Unit: percent · Direction: higher

Uncertainty: type: standard deviation; value: 1.03; n: 3; unit: same as metric

Scored: Not reported · Eligible: Not reported

source checkedGlycanML: A Multi-Task and Multi-Structure Benchmark for Glycan Machine Learning · Table 4, Shallow CNN, TS, column Kingdom

Source checking is not independent reproduction.

RGCN / DWA: SugarBase taxonomy · Order
Configuration: RGCN / DWAProtocol: SugarBase taxonomy · Order (GlycanML taxonomy prediction)Dataset: SugarBase taxonomy · Order

Fixed backbone; compare adaptation strategies, not standalone foundation models. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.

Author-reported evaluation · Evaluation metadata: needs review

45.77(1.03)% accuracy

Unit: percent · Direction: higher

Uncertainty: type: standard deviation; value: 1.03; n: 3; unit: same as metric

Scored: Not reported · Eligible: Not reported

source checkedGlycanML: A Multi-Task and Multi-Structure Benchmark for Glycan Machine Learning · Table 4, RGCN, DWA, column Order

Source checking is not independent reproduction.

Shallow CNN / DTP: SugarBase taxonomy · Mean Acc
Configuration: Shallow CNN / DTPProtocol: SugarBase taxonomy · Mean Acc (GlycanML taxonomy prediction)Dataset: SugarBase taxonomy · Mean Acc

Fixed backbone; compare adaptation strategies, not standalone foundation models. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.

Author-reported evaluation · Evaluation metadata: needs review

62.83(0.45)% mean accuracy across eight taxonomy tasks

Unit: percent · Direction: higher

Uncertainty: type: standard deviation; value: 0.45; n: 3; unit: same as metric

Scored: Not reported · Eligible: Not reported

source checkedGlycanML: A Multi-Task and Multi-Structure Benchmark for Glycan Machine Learning · Table 4, Shallow CNN, DTP, column Mean Acc (%)

Source checking is not independent reproduction.

CompGCN: SugarBase taxonomy · Domain
Configuration: CompGCNProtocol: SugarBase taxonomy · Domain (GlycanML taxonomy prediction)Dataset: SugarBase taxonomy · Domain

Author-reported single-task models on the same held-out task; not a cross-paper ranking. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.

Author-reported evaluation · Evaluation metadata: needs review

93.94(0.41)% accuracy

Unit: percent · Direction: higher

Uncertainty: type: standard deviation; value: 0.41; n: 3; unit: same as metric

Scored: Not reported · Eligible: Not reported

source checkedGlycanML: A Multi-Task and Multi-Structure Benchmark for Glycan Machine Learning · Table 3, row CompGCN, column Domain

Source checking is not independent reproduction.

CompGCN: SugarBase taxonomy · Class
Configuration: CompGCNProtocol: SugarBase taxonomy · Class (GlycanML taxonomy prediction)Dataset: SugarBase taxonomy · Class

Author-reported single-task models on the same held-out task; not a cross-paper ranking. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.

Author-reported evaluation · Evaluation metadata: needs review

69.28(0.99)% accuracy

Unit: percent · Direction: higher

Uncertainty: type: standard deviation; value: 0.99; n: 3; unit: same as metric

Scored: Not reported · Eligible: Not reported

source checkedGlycanML: A Multi-Task and Multi-Structure Benchmark for Glycan Machine Learning · Table 3, row CompGCN, column Class

Source checking is not independent reproduction.

Shallow CNN / N-MTL: SugarBase taxonomy · Mean Acc
Configuration: Shallow CNN / N-MTLProtocol: SugarBase taxonomy · Mean Acc (GlycanML taxonomy prediction)Dataset: SugarBase taxonomy · Mean Acc

Fixed backbone; compare adaptation strategies, not standalone foundation models. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.

Author-reported evaluation · Evaluation metadata: needs review

63.49(0.55)% mean accuracy across eight taxonomy tasks

Unit: percent · Direction: higher

Uncertainty: type: standard deviation; value: 0.55; n: 3; unit: same as metric

Scored: Not reported · Eligible: Not reported

source checkedGlycanML: A Multi-Task and Multi-Structure Benchmark for Glycan Machine Learning · Table 4, Shallow CNN, N-MTL, column Mean Acc (%)

Source checking is not independent reproduction.

Papers and result coverage

Last literature check: 2026-09-17. Primary-paper within-study comparison; numerical results not independently reproduced.

Paper or primary resourceVersionReference
GlycanML: A Multi-Task and Multi-Structure Benchmark for Glycan Machine LearningarXiv:2405.16206v1, 2024-05-25Read source

What is still missing

  • Paper table does not enumerate trained checkpoint hashes; source configuration names retained.
Search and extraction details

complete comparison extracted

Searches

  • GlycanML benchmark 2405.16206

Evidence locations

  • Sections 3–5
  • Tables 1–4

Strengths and limitations

Strengths supported by sources

Limitations and conditions

  • Taxonomy, immunogenicity and glycosylation tasks hold out glycan motif clusters; interaction prediction holds out protein clusters. These boundaries do not imply that both proteins and glycans are unseen in the interaction task.
    Sourcesglycanml primary benchmark evidence · Sections 3.1–3.4 and 5.1; Tables 1 and 3
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Stable record: discovery-benchmark-glycanml-taxonomy-prediction

Evidence table

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50 evidence rows matching the loaded filters

Claims, original sources and review scope · Release 2026-09-17-d277315f7d76
Property and statementOriginal source and locationReview and provenance
Diagram caption

Conceptual procedure. Task variants and protocol versions retain their separate scoring conditions.

Individual claims		GlycanML/GlycanML module/custom_datasets/glycan_classification.py

Original source ↗

Pinned README: Introduction; Experiment configurations; leaderboard; pinned configs/single_task/BERT/species_BERT.yaml, module/custom_datasets/glycan_classification.py (task metric, dataset class and split methods); Sections 3.1–3.3; Table 1

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Retrieved: 2026-09-16T20:43:24.344407+00:00

source checked

automated source review · 2026-09-16

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Primary paper and/or task implementation reviewed for the explicitly cited methodology claims. Scope-limited absence is recorded only after the documented source search; no model runs or independent reproduction.

Field: attributes.profile.diagram.caption

Source artifact SHA-256: 1a385c4afcbfc3d2f1f319cf03fc0f4be5a1e9848cd51f9df4239cbd39771530

Hash scope: Hash scope not separately documented; inspect source record

Inspected artifact

Diagram caption

Conceptual procedure. Task variants and protocol versions retain their separate scoring conditions.

Individual claims		GlycanML/GlycanML configs/single_task/BERT/species_BERT.yaml

Original source ↗

Pinned README: Introduction; Experiment configurations; leaderboard; pinned configs/single_task/BERT/species_BERT.yaml, module/custom_datasets/glycan_classification.py (task metric, dataset class and split methods); Sections 3.1–3.3; Table 1

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Retrieved: 2026-09-16T20:42:46.675170+00:00

source checked

automated source review · 2026-09-16

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Primary paper and/or task implementation reviewed for the explicitly cited methodology claims. Scope-limited absence is recorded only after the documented source search; no model runs or independent reproduction.

Field: attributes.profile.diagram.caption

Source artifact SHA-256: 474995bb150c76479b0c99b6f0b2de49f5b6ad626eae1751e2f53774a4fbf38e

Hash scope: Hash scope not separately documented; inspect source record

Inspected artifact

Diagram caption

Conceptual procedure. Task variants and protocol versions retain their separate scoring conditions.

Individual claims		glycanml primary benchmark evidence

Original source ↗

Pinned README: Introduction; Experiment configurations; leaderboard; pinned configs/single_task/BERT/species_BERT.yaml, module/custom_datasets/glycan_classification.py (task metric, dataset class and split methods); Sections 3.1–3.3; Table 1

Shared locator for this statement’s cited sources; not a separate locator for each citation.

Version: 2405.16206v1
Retrieved: 2026-09-16T21:04:56.017006+00:00

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automated source review · 2026-09-16

Audit details

Primary paper and/or task implementation reviewed for the explicitly cited methodology claims. Scope-limited absence is recorded only after the documented source search; no model runs or independent reproduction.

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Source artifact SHA-256: 9ba3db678b4550898a612b42e8832bf9dee40935090fc4d969ba8f6ac5106979

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Inspected artifact

Diagram caption

Conceptual procedure. Task variants and protocol versions retain their separate scoring conditions.

Individual claims		GlycanML/GlycanML official source

Original source ↗

Pinned README: Introduction; Experiment configurations; leaderboard; pinned configs/single_task/BERT/species_BERT.yaml, module/custom_datasets/glycan_classification.py (task metric, dataset class and split methods); Sections 3.1–3.3; Table 1

Shared locator for this statement’s cited sources; not a separate locator for each citation.

Version: 9f392aa6f9c6d74a296a250199beb347923d04e0
Retrieved: 2026-09-16T10:30:24.094166+00:00

source checked

automated source review · 2026-09-16

Audit details

Primary paper and/or task implementation reviewed for the explicitly cited methodology claims. Scope-limited absence is recorded only after the documented source search; no model runs or independent reproduction.

Field: attributes.profile.diagram.caption

Source artifact SHA-256: 5237cd1af3f1d7b9cf07cfe3cf722987bb27e10ee356ec108584943446bbb836

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Diagram steps

["Allowed inputs: Glycan sequence or graph representation with an organism-taxonomy target.","Splits: Glycans are represented by motif frequencies and clustered; motif groups are allocated to training, validation and test in an 8:1:1 grouping scheme. Table 1 preserves the resulting dataset-specific counts.","Metrics: Accuracy and Matthews correlation coefficient in the checked species-classification configuration."]

Individual claims		GlycanML/GlycanML module/custom_datasets/glycan_classification.py

Original source ↗

Pinned README: Introduction; Experiment configurations; leaderboard; pinned configs/single_task/BERT/species_BERT.yaml, module/custom_datasets/glycan_classification.py (task metric, dataset class and split methods); Sections 3.1–3.3; Table 1

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Retrieved: 2026-09-16T20:43:24.344407+00:00

source checked

automated source review · 2026-09-16

Audit details

Primary paper and/or task implementation reviewed for the explicitly cited methodology claims. Scope-limited absence is recorded only after the documented source search; no model runs or independent reproduction.

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Source artifact SHA-256: 1a385c4afcbfc3d2f1f319cf03fc0f4be5a1e9848cd51f9df4239cbd39771530

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Inspected artifact

Diagram steps

["Allowed inputs: Glycan sequence or graph representation with an organism-taxonomy target.","Splits: Glycans are represented by motif frequencies and clustered; motif groups are allocated to training, validation and test in an 8:1:1 grouping scheme. Table 1 preserves the resulting dataset-specific counts.","Metrics: Accuracy and Matthews correlation coefficient in the checked species-classification configuration."]

Individual claims		GlycanML/GlycanML configs/single_task/BERT/species_BERT.yaml

Original source ↗

Pinned README: Introduction; Experiment configurations; leaderboard; pinned configs/single_task/BERT/species_BERT.yaml, module/custom_datasets/glycan_classification.py (task metric, dataset class and split methods); Sections 3.1–3.3; Table 1

Shared locator for this statement’s cited sources; not a separate locator for each citation.

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Retrieved: 2026-09-16T20:42:46.675170+00:00

source checked

automated source review · 2026-09-16

Audit details

Primary paper and/or task implementation reviewed for the explicitly cited methodology claims. Scope-limited absence is recorded only after the documented source search; no model runs or independent reproduction.

Field: attributes.profile.diagram.steps

Source artifact SHA-256: 474995bb150c76479b0c99b6f0b2de49f5b6ad626eae1751e2f53774a4fbf38e

Hash scope: Hash scope not separately documented; inspect source record

Inspected artifact

Diagram steps

["Allowed inputs: Glycan sequence or graph representation with an organism-taxonomy target.","Splits: Glycans are represented by motif frequencies and clustered; motif groups are allocated to training, validation and test in an 8:1:1 grouping scheme. Table 1 preserves the resulting dataset-specific counts.","Metrics: Accuracy and Matthews correlation coefficient in the checked species-classification configuration."]

Individual claims		glycanml primary benchmark evidence

Original source ↗

Pinned README: Introduction; Experiment configurations; leaderboard; pinned configs/single_task/BERT/species_BERT.yaml, module/custom_datasets/glycan_classification.py (task metric, dataset class and split methods); Sections 3.1–3.3; Table 1

Shared locator for this statement’s cited sources; not a separate locator for each citation.

Version: 2405.16206v1
Retrieved: 2026-09-16T21:04:56.017006+00:00

source checked

automated source review · 2026-09-16

Audit details

Primary paper and/or task implementation reviewed for the explicitly cited methodology claims. Scope-limited absence is recorded only after the documented source search; no model runs or independent reproduction.

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Source artifact SHA-256: 9ba3db678b4550898a612b42e8832bf9dee40935090fc4d969ba8f6ac5106979

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Inspected artifact

Diagram steps

["Allowed inputs: Glycan sequence or graph representation with an organism-taxonomy target.","Splits: Glycans are represented by motif frequencies and clustered; motif groups are allocated to training, validation and test in an 8:1:1 grouping scheme. Table 1 preserves the resulting dataset-specific counts.","Metrics: Accuracy and Matthews correlation coefficient in the checked species-classification configuration."]

Individual claims		GlycanML/GlycanML official source

Original source ↗

Pinned README: Introduction; Experiment configurations; leaderboard; pinned configs/single_task/BERT/species_BERT.yaml, module/custom_datasets/glycan_classification.py (task metric, dataset class and split methods); Sections 3.1–3.3; Table 1

Shared locator for this statement’s cited sources; not a separate locator for each citation.

Version: 9f392aa6f9c6d74a296a250199beb347923d04e0
Retrieved: 2026-09-16T10:30:24.094166+00:00

source checked

automated source review · 2026-09-16

Audit details

Primary paper and/or task implementation reviewed for the explicitly cited methodology claims. Scope-limited absence is recorded only after the documented source search; no model runs or independent reproduction.

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Source artifact SHA-256: 5237cd1af3f1d7b9cf07cfe3cf722987bb27e10ee356ec108584943446bbb836

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Diagram title

Evaluation procedure

Individual claims		GlycanML/GlycanML module/custom_datasets/glycan_classification.py

Original source ↗

Pinned README: Introduction; Experiment configurations; leaderboard; pinned configs/single_task/BERT/species_BERT.yaml, module/custom_datasets/glycan_classification.py (task metric, dataset class and split methods); Sections 3.1–3.3; Table 1

Shared locator for this statement’s cited sources; not a separate locator for each citation.

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Retrieved: 2026-09-16T20:43:24.344407+00:00

source checked

automated source review · 2026-09-16

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Primary paper and/or task implementation reviewed for the explicitly cited methodology claims. Scope-limited absence is recorded only after the documented source search; no model runs or independent reproduction.

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Source artifact SHA-256: 1a385c4afcbfc3d2f1f319cf03fc0f4be5a1e9848cd51f9df4239cbd39771530

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Inspected artifact

Diagram title

Evaluation procedure

Individual claims		GlycanML/GlycanML configs/single_task/BERT/species_BERT.yaml

Original source ↗

Pinned README: Introduction; Experiment configurations; leaderboard; pinned configs/single_task/BERT/species_BERT.yaml, module/custom_datasets/glycan_classification.py (task metric, dataset class and split methods); Sections 3.1–3.3; Table 1

Shared locator for this statement’s cited sources; not a separate locator for each citation.

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Retrieved: 2026-09-16T20:42:46.675170+00:00

source checked

automated source review · 2026-09-16

Audit details

Primary paper and/or task implementation reviewed for the explicitly cited methodology claims. Scope-limited absence is recorded only after the documented source search; no model runs or independent reproduction.

Field: attributes.profile.diagram.title

Source artifact SHA-256: 474995bb150c76479b0c99b6f0b2de49f5b6ad626eae1751e2f53774a4fbf38e

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Sources and history

Release 2026-09-17-d277315f7d76 · Record review: discovered

5 source records and release historyDownload this release
Technical metadata and extraction receipts

Stable ID: discovery-benchmark-glycanml-taxonomy-prediction

areas
glycomics
entity level
task
scope note
Specialist molecular or omics evaluation; protocol details require review before numerical comparison.
task
taxonomy prediction
version
Not reported
comparison panels
id: glycanml-v1-single-domain; title: GlycanML Domain · Table 3, p. 8; protocol id: paper-protocol-b51570e2bf69ba6542; dataset id: paper-dataset-ce710f303c24cb91c2; metric: accuracy; unit: percent; direction: higher; result ids: paper-result-075ca33548bf92ef7e; paper-result-0b5a6c78c01691f3ee; paper-result-d0bee0d884f958e49b; paper-result-4543142c4187cbea18; paper-result-ea2bf935a59e8b5693; paper-result-a4c79e5939d76f3983; paper-result-c3d21b293a8aa00c67; paper-result-5c30f19e8d1c4b91ca; paper-result-2aed0631283783ea66; paper-result-22b89e6bc2ccf2fe1b; source ids: expansion-p3-glycanml-2405-16206v1; source locator: Table 3, p. 8, Domain; context: Author-reported single-task models on the same held-out task; not a cross-paper ranking. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.; caveats: Taxonomic levels are distinct tasks; do not combine raw accuracy across levels.; Protein–glycan scores evaluate combined glycan encoders plus ESM-1b/MLP, not an ESM-1b standalone checkpoint.; Cluster manifests and trained checkpoint hashes are not enumerated in the paper table.; review: method: automated_source_review; date: 2026-09-17; id: glycanml-v1-single-kingdom; title: GlycanML Kingdom · Table 3, p. 8; protocol id: paper-protocol-468fdd0f4237c041d5; dataset id: paper-dataset-d7ac4e0285fbaefcab; metric: accuracy; unit: percent; direction: higher; result ids: paper-result-09a8ee00d327d0e580; paper-result-f088c48c3867898d15; paper-result-362d36486fd109e109; paper-result-a50f7f1f26ebaa5d42; paper-result-45611bc39c803891d0; paper-result-f99f24cc69642bce07; paper-result-a0c917c8b7a0bf0388; paper-result-410bae2881489d32dd; paper-result-8dea7f67a6307f6e1f; paper-result-98203c7be8a19baf6c; source ids: expansion-p3-glycanml-2405-16206v1; source locator: Table 3, p. 8, Kingdom; context: Author-reported single-task models on the same held-out task; not a cross-paper ranking. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.; caveats: Taxonomic levels are distinct tasks; do not combine raw accuracy across levels.; Protein–glycan scores evaluate combined glycan encoders plus ESM-1b/MLP, not an ESM-1b standalone checkpoint.; Cluster manifests and trained checkpoint hashes are not enumerated in the paper table.; review: method: automated_source_review; date: 2026-09-17; id: glycanml-v1-single-phylum; title: GlycanML Phylum · Table 3, p. 8; protocol id: paper-protocol-a67c6f1eaf73494953; dataset id: paper-dataset-13c71587fb39174bb1; metric: accuracy; unit: percent; direction: higher; result ids: paper-result-86a3427fdae5f397c2; paper-result-c861ec21227123bf0b; paper-result-c66956e19c73fa9abf; paper-result-c1f20eec6e0881c783; paper-result-82156f189d0b3f8cd9; paper-result-3e5677e0c95596dcd7; paper-result-34a6e2a047a75ce39d; paper-result-2dfea46ca6f79b2ec1; paper-result-778ad7ec5934e369ed; paper-result-c99e02301c02669cc1; source ids: expansion-p3-glycanml-2405-16206v1; source locator: Table 3, p. 8, Phylum; context: Author-reported single-task models on the same held-out task; not a cross-paper ranking. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.; caveats: Taxonomic levels are distinct tasks; do not combine raw accuracy across levels.; Protein–glycan scores evaluate combined glycan encoders plus ESM-1b/MLP, not an ESM-1b standalone checkpoint.; Cluster manifests and trained checkpoint hashes are not enumerated in the paper table.; review: method: automated_source_review; date: 2026-09-17; id: glycanml-v1-single-class; title: GlycanML Class · Table 3, p. 8; protocol id: paper-protocol-a8799c4d8546b3ff18; dataset id: paper-dataset-f9b7538618a3e1ef5b; metric: accuracy; unit: percent; direction: higher; result ids: paper-result-32ae4afd9a4c6cc89b; paper-result-da7afa1436a5e1fae4; paper-result-504992911a85e182cc; paper-result-c459dd0862f1e48ee5; paper-result-2b302ea797608740bf; paper-result-966ff1085af74c7b89; paper-result-524b419436ec9d8192; paper-result-d9c96e1c04f27b0d48; paper-result-ed1c70949ef07f3093; paper-result-233bc2d9c1d6575a1e; source ids: expansion-p3-glycanml-2405-16206v1; source locator: Table 3, p. 8, Class; context: Author-reported single-task models on the same held-out task; not a cross-paper ranking. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.; caveats: Taxonomic levels are distinct tasks; do not combine raw accuracy across levels.; Protein–glycan scores evaluate combined glycan encoders plus ESM-1b/MLP, not an ESM-1b standalone checkpoint.; Cluster manifests and trained checkpoint hashes are not enumerated in the paper table.; review: method: automated_source_review; date: 2026-09-17; id: glycanml-v1-single-order; title: GlycanML Order · Table 3, p. 8; protocol id: paper-protocol-fb9190db65a5f72e10; dataset id: paper-dataset-4298ac38e85e6f8048; metric: accuracy; unit: percent; direction: higher; result ids: paper-result-abee54829f2ea08080; paper-result-7281a16570ce97b5cb; paper-result-cefa58a1f691f86ec4; paper-result-35cbadb92d47ea3c60; paper-result-3f3f4af93e3f57eacd; paper-result-0d4e92bac0d910c607; paper-result-3a9fc20851e90815ea; paper-result-d8d2084714e258967b; paper-result-ac7e4ed00d0e9515da; paper-result-af527f9d744c668717; source ids: expansion-p3-glycanml-2405-16206v1; source locator: Table 3, p. 8, Order; context: Author-reported single-task models on the same held-out task; not a cross-paper ranking. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.; caveats: Taxonomic levels are distinct tasks; do not combine raw accuracy across levels.; Protein–glycan scores evaluate combined glycan encoders plus ESM-1b/MLP, not an ESM-1b standalone checkpoint.; Cluster manifests and trained checkpoint hashes are not enumerated in the paper table.; review: method: automated_source_review; date: 2026-09-17; id: glycanml-v1-single-family; title: GlycanML Family · Table 3, p. 8; protocol id: paper-protocol-8dca9eabea26dee766; dataset id: paper-dataset-8d5d79e4a6a070bfff; metric: accuracy; unit: percent; direction: higher; result ids: paper-result-09e347c7674f561706; paper-result-8bbf97e6e23787da99; paper-result-f5c63db202d546dd8a; paper-result-941024aaffa398c454; paper-result-32c123a2e27ebbe79e; paper-result-4a140ae1cf5519925c; paper-result-76cbc3d2466076b944; paper-result-42fcbda81a387d226d; paper-result-e4ad7f8d96df00ea4b; paper-result-1337bae859236e6dcf; source ids: expansion-p3-glycanml-2405-16206v1; source locator: Table 3, p. 8, Family; context: Author-reported single-task models on the same held-out task; not a cross-paper ranking. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.; caveats: Taxonomic levels are distinct tasks; do not combine raw accuracy across levels.; Protein–glycan scores evaluate combined glycan encoders plus ESM-1b/MLP, not an ESM-1b standalone checkpoint.; Cluster manifests and trained checkpoint hashes are not enumerated in the paper table.; review: method: automated_source_review; date: 2026-09-17; id: glycanml-v1-single-genus; title: GlycanML Genus · Table 3, p. 8; protocol id: paper-protocol-d44b40251c33ecbea1; dataset id: paper-dataset-df6da272a0a4d8de8f; metric: accuracy; unit: percent; direction: higher; result ids: paper-result-ca073eb7b8ec09d81b; paper-result-ec1560d4f26c854db8; paper-result-012e11422e843b97f7; paper-result-6d2f9510b6bb3023ca; paper-result-6a9cd20bbb10eaeb7e; paper-result-5e1004788b7b7c0281; paper-result-f3d9c9bc60ec3b9cc7; paper-result-e30b9280e6a5a6fd1c; paper-result-fe8185d4fa2fa84e4e; paper-result-fae731c13691351a52; source ids: expansion-p3-glycanml-2405-16206v1; source locator: Table 3, p. 8, Genus; context: Author-reported single-task models on the same held-out task; not a cross-paper ranking. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.; caveats: Taxonomic levels are distinct tasks; do not combine raw accuracy across levels.; Protein–glycan scores evaluate combined glycan encoders plus ESM-1b/MLP, not an ESM-1b standalone checkpoint.; Cluster manifests and trained checkpoint hashes are not enumerated in the paper table.; review: method: automated_source_review; date: 2026-09-17; id: glycanml-v1-single-species; title: GlycanML Species · Table 3, p. 8; protocol id: paper-protocol-0d66c7cabff9d799d5; dataset id: paper-dataset-4c48251022ccb53313; metric: accuracy; unit: percent; direction: higher; result ids: paper-result-dc5501dc1954b54a36; paper-result-825d6b6597e463e7cc; paper-result-fedc9b0d7fadb8f74e; paper-result-19f2505d003bc76dcc; paper-result-e9a6b117f8a49863be; paper-result-33876cb9abd2083b08; paper-result-fad4c5f6d2c179bb3f; paper-result-77f9c63846886893da; paper-result-723b93ccacba688198; paper-result-e74c30428cded73273; source ids: expansion-p3-glycanml-2405-16206v1; source locator: Table 3, p. 8, Species; context: Author-reported single-task models on the same held-out task; not a cross-paper ranking. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.; caveats: Taxonomic levels are distinct tasks; do not combine raw accuracy across levels.; Protein–glycan scores evaluate combined glycan encoders plus ESM-1b/MLP, not an ESM-1b standalone checkpoint.; Cluster manifests and trained checkpoint hashes are not enumerated in the paper table.; review: method: automated_source_review; date: 2026-09-17; id: glycanml-v1-mtl-0-domain; title: GlycanML Domain · Table 4, p. 9; protocol id: paper-protocol-b51570e2bf69ba6542; dataset id: paper-dataset-ce710f303c24cb91c2; metric: accuracy; unit: percent; direction: higher; result ids: paper-result-075ca33548bf92ef7e; paper-result-fdb107a7ebf70f41b9; paper-result-0f87601e912c4389c4; paper-result-cea1fd4ede7da082f0; paper-result-a1d4fa5ae3eeb76bef; paper-result-425410a32e12403336; paper-result-7d6bfd8ae273536a43; source ids: expansion-p3-glycanml-2405-16206v1; source locator: Table 4, p. 9, Domain; context: Fixed backbone; compare adaptation strategies, not standalone foundation models. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.; caveats: Mean accuracy is an unweighted aggregate of eight taxonomic tasks as reported; keep separate from every individual level.; The six MTL approaches change training, so do not label their scores as the base encoder alone.; review: method: automated_source_review; date: 2026-09-17; id: glycanml-v1-mtl-0-kingdom; title: GlycanML Kingdom · Table 4, p. 9; protocol id: paper-protocol-468fdd0f4237c041d5; dataset id: paper-dataset-d7ac4e0285fbaefcab; metric: accuracy; unit: percent; direction: higher; result ids: paper-result-09a8ee00d327d0e580; paper-result-b8f593d66e81d8176a; paper-result-8ac1e91e821535aaff; paper-result-1c68188144c78ba223; paper-result-59aaa70a642c016c71; paper-result-3a2676e4651199a4c7; paper-result-5f6263a893e28a7406; source ids: expansion-p3-glycanml-2405-16206v1; source locator: Table 4, p. 9, Kingdom; context: Fixed backbone; compare adaptation strategies, not standalone foundation models. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.; caveats: Mean accuracy is an unweighted aggregate of eight taxonomic tasks as reported; keep separate from every individual level.; The six MTL approaches change training, so do not label their scores as the base encoder alone.; review: method: automated_source_review; date: 2026-09-17; id: glycanml-v1-mtl-0-phylum; title: GlycanML Phylum · Table 4, p. 9; protocol id: paper-protocol-a67c6f1eaf73494953; dataset id: paper-dataset-13c71587fb39174bb1; metric: accuracy; unit: percent; direction: higher; result ids: paper-result-86a3427fdae5f397c2; paper-result-8c1657f118a2dd808c; paper-result-ad0d1dd5c3cfc326af; paper-result-9dc690711bc090f19a; paper-result-a0214b935eba01b7a7; paper-result-43fa9c20d6d6b41325; paper-result-aec7ac1f09709fe2e2; source ids: expansion-p3-glycanml-2405-16206v1; source locator: Table 4, p. 9, Phylum; context: Fixed backbone; compare adaptation strategies, not standalone foundation models. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.; caveats: Mean accuracy is an unweighted aggregate of eight taxonomic tasks as reported; keep separate from every individual level.; The six MTL approaches change training, so do not label their scores as the base encoder alone.; review: method: automated_source_review; date: 2026-09-17; id: glycanml-v1-mtl-0-class; title: GlycanML Class · Table 4, p. 9; protocol id: paper-protocol-a8799c4d8546b3ff18; dataset id: paper-dataset-f9b7538618a3e1ef5b; metric: accuracy; unit: percent; direction: higher; result ids: paper-result-32ae4afd9a4c6cc89b; paper-result-0bf3b2d166c172f494; paper-result-5cf336e78e8b9646f7; paper-result-3aa13c82aac4f30398; paper-result-750f1db97156302340; paper-result-50d6f0ba85fc1e572d; paper-result-5e2c55226c432a927b; source ids: expansion-p3-glycanml-2405-16206v1; source locator: Table 4, p. 9, Class; context: Fixed backbone; compare adaptation strategies, not standalone foundation models. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.; caveats: Mean accuracy is an unweighted aggregate of eight taxonomic tasks as reported; keep separate from every individual level.; The six MTL approaches change training, so do not label their scores as the base encoder alone.; review: method: automated_source_review; date: 2026-09-17; id: glycanml-v1-mtl-0-order; title: GlycanML Order · Table 4, p. 9; protocol id: paper-protocol-fb9190db65a5f72e10; dataset id: paper-dataset-4298ac38e85e6f8048; metric: accuracy; unit: percent; direction: higher; result ids: paper-result-abee54829f2ea08080; paper-result-a299bb2064e93cd095; paper-result-56157f637f3dc28dde; paper-result-d72371bae7104d40bb; paper-result-6692b5f2ab128e869f; paper-result-b20abedfaf179beb0f; paper-result-b1a73f13e672f9f010; source ids: expansion-p3-glycanml-2405-16206v1; source locator: Table 4, p. 9, Order; context: Fixed backbone; compare adaptation strategies, not standalone foundation models. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.; caveats: Mean accuracy is an unweighted aggregate of eight taxonomic tasks as reported; keep separate from every individual level.; The six MTL approaches change training, so do not label their scores as the base encoder alone.; review: method: automated_source_review; date: 2026-09-17; id: glycanml-v1-mtl-0-family; title: GlycanML Family · Table 4, p. 9; protocol id: paper-protocol-8dca9eabea26dee766; dataset id: paper-dataset-8d5d79e4a6a070bfff; metric: accuracy; unit: percent; direction: higher; result ids: paper-result-09e347c7674f561706; paper-result-89df39b51fc8f73a18; paper-result-3710d0e01f022a8f58; paper-result-76b9f005da338ff5f4; paper-result-97cf3af868ea202c20; paper-result-0f26b2626d93d81e1a; paper-result-45cd9dbf505c6c3edf; source ids: expansion-p3-glycanml-2405-16206v1; source locator: Table 4, p. 9, Family; context: Fixed backbone; compare adaptation strategies, not standalone foundation models. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.; caveats: Mean accuracy is an unweighted aggregate of eight taxonomic tasks as reported; keep separate from every individual level.; The six MTL approaches change training, so do not label their scores as the base encoder alone.; review: method: automated_source_review; date: 2026-09-17; id: glycanml-v1-mtl-0-genus; title: GlycanML Genus · Table 4, p. 9; protocol id: paper-protocol-d44b40251c33ecbea1; dataset id: paper-dataset-df6da272a0a4d8de8f; metric: accuracy; unit: percent; direction: higher; result ids: paper-result-ca073eb7b8ec09d81b; paper-result-658a0bde785ae82791; paper-result-ba1188eafda83f2dff; paper-result-8ef3992de76e028698; paper-result-72ccde0fe45e939979; paper-result-4dc4b5b8e2b2f51f0d; paper-result-e2393930743e0fe2a3; source ids: expansion-p3-glycanml-2405-16206v1; source locator: Table 4, p. 9, Genus; context: Fixed backbone; compare adaptation strategies, not standalone foundation models. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.; caveats: Mean accuracy is an unweighted aggregate of eight taxonomic tasks as reported; keep separate from every individual level.; The six MTL approaches change training, so do not label their scores as the base encoder alone.; review: method: automated_source_review; date: 2026-09-17; id: glycanml-v1-mtl-0-species; title: GlycanML Species · Table 4, p. 9; protocol id: paper-protocol-0d66c7cabff9d799d5; dataset id: paper-dataset-4c48251022ccb53313; metric: accuracy; unit: percent; direction: higher; result ids: paper-result-dc5501dc1954b54a36; paper-result-3decc92d3c4b47daaa; paper-result-efe8061d8b717b3d06; paper-result-994aa15ef63ab963bd; paper-result-af7e44b66703958045; paper-result-7741da71a6bffbfe05; paper-result-2a425531358cee050f; source ids: expansion-p3-glycanml-2405-16206v1; source locator: Table 4, p. 9, Species; context: Fixed backbone; compare adaptation strategies, not standalone foundation models. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.; caveats: Mean accuracy is an unweighted aggregate of eight taxonomic tasks as reported; keep separate from every individual level.; The six MTL approaches change training, so do not label their scores as the base encoder alone.; review: method: automated_source_review; date: 2026-09-17; id: glycanml-v1-mtl-0-mean-acc; title: GlycanML Mean Acc (%) · Table 4, p. 9; protocol id: paper-protocol-f6058db65ace02f920; dataset id: paper-dataset-3b08dc110e25f5011e; metric: mean accuracy across eight taxonomy tasks; unit: percent; direction: higher; result ids: paper-result-3b8254d638b7cf840c; paper-result-26353885331e3500b0; paper-result-14f9374f43c9937375; paper-result-37820120325451b083; paper-result-010096d9a694ae3520; paper-result-96a241380ae6ba882a; paper-result-222b0816ea58c393cd; source ids: expansion-p3-glycanml-2405-16206v1; source locator: Table 4, p. 9, Mean Acc (%); context: Fixed backbone; compare adaptation strategies, not standalone foundation models. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.; caveats: Mean accuracy is an unweighted aggregate of eight taxonomic tasks as reported; keep separate from every individual level.; The six MTL approaches change training, so do not label their scores as the base encoder alone.; review: method: automated_source_review; date: 2026-09-17; id: glycanml-v1-mtl-1-domain; title: GlycanML Domain · Table 4, p. 9; protocol id: paper-protocol-b51570e2bf69ba6542; dataset id: paper-dataset-ce710f303c24cb91c2; metric: accuracy; unit: percent; direction: higher; result ids: paper-result-2aed0631283783ea66; paper-result-944ec4d9e5d591defb; paper-result-976c07b788d0c976b5; paper-result-db9fa9da2f1bc0b208; paper-result-f0d520d129d1982892; paper-result-abafa8cbc9111307ef; paper-result-c1ebf0846e005b0ea7; source ids: expansion-p3-glycanml-2405-16206v1; source locator: Table 4, p. 9, Domain; context: Fixed backbone; compare adaptation strategies, not standalone foundation models. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.; caveats: Mean accuracy is an unweighted aggregate of eight taxonomic tasks as reported; keep separate from every individual level.; The six MTL approaches change training, so do not label their scores as the base encoder alone.; review: method: automated_source_review; date: 2026-09-17; id: glycanml-v1-mtl-1-kingdom; title: GlycanML Kingdom · Table 4, p. 9; protocol id: paper-protocol-468fdd0f4237c041d5; dataset id: paper-dataset-d7ac4e0285fbaefcab; metric: accuracy; unit: percent; direction: higher; result ids: paper-result-8dea7f67a6307f6e1f; paper-result-89b973f6b24d20d70b; paper-result-7c4ad10784c12bc139; paper-result-853910c368572b5ab8; paper-result-0fd9b263d4d481168c; paper-result-a189d3bce6f0744a64; paper-result-c1581b8b54883511ea; source ids: expansion-p3-glycanml-2405-16206v1; source locator: Table 4, p. 9, Kingdom; context: Fixed backbone; compare adaptation strategies, not standalone foundation models. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.; caveats: Mean accuracy is an unweighted aggregate of eight taxonomic tasks as reported; keep separate from every individual level.; The six MTL approaches change training, so do not label their scores as the base encoder alone.; review: method: automated_source_review; date: 2026-09-17; id: glycanml-v1-mtl-1-phylum; title: GlycanML Phylum · Table 4, p. 9; protocol id: paper-protocol-a67c6f1eaf73494953; dataset id: paper-dataset-13c71587fb39174bb1; metric: accuracy; unit: percent; direction: higher; result ids: paper-result-778ad7ec5934e369ed; paper-result-d60a1868d6cac6686d; paper-result-3faf014c06fdb5b338; paper-result-6a836283cecd69f216; paper-result-077aee2f6ad33075b7; paper-result-26eb6ff8bcba531860; paper-result-8776fd8149834a3faf; source ids: expansion-p3-glycanml-2405-16206v1; source locator: Table 4, p. 9, Phylum; context: Fixed backbone; compare adaptation strategies, not standalone foundation models. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.; caveats: Mean accuracy is an unweighted aggregate of eight taxonomic tasks as reported; keep separate from every individual level.; The six MTL approaches change training, so do not label their scores as the base encoder alone.; review: method: automated_source_review; date: 2026-09-17; id: glycanml-v1-mtl-1-class; title: GlycanML Class · Table 4, p. 9; protocol id: paper-protocol-a8799c4d8546b3ff18; dataset id: paper-dataset-f9b7538618a3e1ef5b; metric: accuracy; unit: percent; direction: higher; result ids: paper-result-ed1c70949ef07f3093; paper-result-c4239ca0a78748ef44; paper-result-d6364ea9b2a3f8fdaf; paper-result-34b64c1f78f1731268; paper-result-8f14f53fefa6b454b9; paper-result-42546bacf62f9b3a69; paper-result-7a11fb3230b1f4dcec; source ids: expansion-p3-glycanml-2405-16206v1; source locator: Table 4, p. 9, Class; context: Fixed backbone; compare adaptation strategies, not standalone foundation models. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.; caveats: Mean accuracy is an unweighted aggregate of eight taxonomic tasks as reported; keep separate from every individual level.; The six MTL approaches change training, so do not label their scores as the base encoder alone.; review: method: automated_source_review; date: 2026-09-17; id: glycanml-v1-mtl-1-order; title: GlycanML Order · Table 4, p. 9; protocol id: paper-protocol-fb9190db65a5f72e10; dataset id: paper-dataset-4298ac38e85e6f8048; metric: accuracy; unit: percent; direction: higher; result ids: paper-result-ac7e4ed00d0e9515da; paper-result-e6c210784feca5b6d4; paper-result-f5553ce1aa89330367; paper-result-57e2eb21a87ce0ad82; paper-result-f8ab2a4236081aaa2d; paper-result-200cf342b98083f8eb; paper-result-6abd4dddb7fdcb0691; source ids: expansion-p3-glycanml-2405-16206v1; source locator: Table 4, p. 9, Order; context: Fixed backbone; compare adaptation strategies, not standalone foundation models. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.; caveats: Mean accuracy is an unweighted aggregate of eight taxonomic tasks as reported; keep separate from every individual level.; The six MTL approaches change training, so do not label their scores as the base encoder alone.; review: method: automated_source_review; date: 2026-09-17; id: glycanml-v1-mtl-1-family; title: GlycanML Family · Table 4, p. 9; protocol id: paper-protocol-8dca9eabea26dee766; dataset id: paper-dataset-8d5d79e4a6a070bfff; metric: accuracy; unit: percent; direction: higher; result ids: paper-result-e4ad7f8d96df00ea4b; paper-result-7e5eb5e5def1997d2a; paper-result-6189ade0e2553e6a5f; paper-result-850116eec75fde4e16; paper-result-a90400ff944a9dba82; paper-result-4089bd143f1ec302b3; paper-result-b691a9c8c205144c7f; source ids: expansion-p3-glycanml-2405-16206v1; source locator: Table 4, p. 9, Family; context: Fixed backbone; compare adaptation strategies, not standalone foundation models. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.; caveats: Mean accuracy is an unweighted aggregate of eight taxonomic tasks as reported; keep separate from every individual level.; The six MTL approaches change training, so do not label their scores as the base encoder alone.; review: method: automated_source_review; date: 2026-09-17; id: glycanml-v1-mtl-1-genus; title: GlycanML Genus · Table 4, p. 9; protocol id: paper-protocol-d44b40251c33ecbea1; dataset id: paper-dataset-df6da272a0a4d8de8f; metric: accuracy; unit: percent; direction: higher; result ids: paper-result-fe8185d4fa2fa84e4e; paper-result-14bd301ffa5bdd2ba0; paper-result-6ae0a365e6b33ffa9b; paper-result-fc6174c38a0298468e; paper-result-410dcdb82f6d6c3b0f; paper-result-54ce873648167ddf7f; paper-result-aa50763240e086a7b0; source ids: expansion-p3-glycanml-2405-16206v1; source locator: Table 4, p. 9, Genus; context: Fixed backbone; compare adaptation strategies, not standalone foundation models. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.; caveats: Mean accuracy is an unweighted aggregate of eight taxonomic tasks as reported; keep separate from every individual level.; The six MTL approaches change training, so do not label their scores as the base encoder alone.; review: method: automated_source_review; date: 2026-09-17; id: glycanml-v1-mtl-1-species; title: GlycanML Species · Table 4, p. 9; protocol id: paper-protocol-0d66c7cabff9d799d5; dataset id: paper-dataset-4c48251022ccb53313; metric: accuracy; unit: percent; direction: higher; result ids: paper-result-723b93ccacba688198; paper-result-ec8c144273ccc0a850; paper-result-928828d65bf0d96282; paper-result-5f765144a6d6d5bdb4; paper-result-dc366357aed61c1324; paper-result-618be13e9cdea80f33; paper-result-e3de6f13924b3f0956; source ids: expansion-p3-glycanml-2405-16206v1; source locator: Table 4, p. 9, Species; context: Fixed backbone; compare adaptation strategies, not standalone foundation models. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.; caveats: Mean accuracy is an unweighted aggregate of eight taxonomic tasks as reported; keep separate from every individual level.; The six MTL approaches change training, so do not label their scores as the base encoder alone.; review: method: automated_source_review; date: 2026-09-17; id: glycanml-v1-mtl-1-mean-acc; title: GlycanML Mean Acc (%) · Table 4, p. 9; protocol id: paper-protocol-f6058db65ace02f920; dataset id: paper-dataset-3b08dc110e25f5011e; metric: mean accuracy across eight taxonomy tasks; unit: percent; direction: higher; result ids: paper-result-5ebd480b2815e83e3d; paper-result-13c2836aee0f4143ea; paper-result-19abc6d9c3e6eaf449; paper-result-16f47917baa3903d08; paper-result-2d85ed1325988db66d; paper-result-31eba8c1c674dcaf66; paper-result-ecd8e5a52b2ee62bca; source ids: expansion-p3-glycanml-2405-16206v1; source locator: Table 4, p. 9, Mean Acc (%); context: Fixed backbone; compare adaptation strategies, not standalone foundation models. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.; caveats: Mean accuracy is an unweighted aggregate of eight taxonomic tasks as reported; keep separate from every individual level.; The six MTL approaches change training, so do not label their scores as the base encoder alone.; review: method: automated_source_review; date: 2026-09-17
benchmark research
review date: 2026-09-17; status: complete_comparison_extracted; primary sources: expansion-p3-glycanml-2405-16206v1; inspected locators: Sections 3–5; Tables 1–4; searched queries: GlycanML benchmark 2405.16206; gaps: Paper table does not enumerate trained checkpoint hashes; source configuration names retained.; claim scope: Primary-paper within-study comparison; numerical results not independently reproduced.
historical missing metadata
dataset release: unextracted; metric implementation: unextracted; split manifest: unextracted; version: unextracted
metadata review scope
historical_missing_metadata preserves the original discovery state. Current descriptive evidence and missingness are recorded in profile.facts; numerical-result review is separate.
legacy kinds
benchmark
entity classification
review date: 2026-09-17; rationale: This record identifies the biological prediction question or a suite-specific task, rather than a uniquely fixed evaluated procedure. Preserve its task identity and leave split, model adaptation and scoring details on linked protocols/evaluations.; source ids: src-discovery-glycanml-glycanml; evidence-benchmark-glycanml-bert-species-bert-yaml; evidence-benchmark-glycan-classification-dataset; source locator: Pinned README: Introduction; Experiment configurations; leaderboard; pinned configs/single_task/BERT/species_BERT.yaml, module/custom_datasets/glycan_classification.py (task metric, dataset class and split methods); ambiguities: A paper- or suite-specific task may constrain some inputs or metrics; that alone does not make it interchangeable with a complete versioned protocol. No protocol equivalence is inferred.
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