Strengths and considerations
No source-reviewed explanatory claims are recorded here yet.
Task
Hierarchical metagenomic taxonomy classification
Hierarchical taxonomic classification evaluates sequence labels under separate in-distribution and out-of-distribution settings.
Sources
ICCTax: a hierarchical taxonomic classifier for metagenomic sequences on a large language model · Results §§3.1–3.2.1; cached text lines 47–48, 51, 53–54; uncertainty/repeat-run/statistical-comparison passagesAt a glance
Inputs, training, access and other details
Explanatory profile: limited source coverage · Automated source review, 2026-09-16. Review applies to the cited claims; unresolved fields are listed below. Numerical results retain their own review status.
| Property | Description and evidence |
|---|---|
| Datasets | BERTax-derived ID, OOD and Complete datasets spanning major taxonomic groups.SourcesICCTax: a hierarchical taxonomic classifier for metagenomic sequences on a large language model · Results §§3.1–3.2.1; cached text lines 47–48, 51, 53–54; uncertainty/repeat-run/statistical-comparison passages |
| Splits | ID reserves 2,000 sequences per phylum for testing and uses the rest for training. OOD assigns every sequence in a genus to a single side of the train/test partition. The Complete-dataset classifier is a separate setting.SourcesICCTax: a hierarchical taxonomic classifier for metagenomic sequences on a large language model · §3.1 Datasets; Fig.2 caption (a–c) |
| Metrics | Accuracy and macro average precision at superkingdom and phylum levels; micro/macro AUC appears in supplements.SourcesICCTax: a hierarchical taxonomic classifier for metagenomic sequences on a large language model · Results §§3.1–3.2.1; cached text lines 47–48, 51, 53–54; uncertainty/repeat-run/statistical-comparison passages |
| Baselines | MMseqs2, Minimap2, Kraken2, sourmash, MetaPhlAn4, CAT, DeepMicrobes and BERTax.SourcesICCTax: a hierarchical taxonomic classifier for metagenomic sequences on a large language model · Results §§3.1–3.2.1; cached text lines 47–48, 51, 53–54; uncertainty/repeat-run/statistical-comparison passages |
| Leakage controls | Figure 2 defines ID testing by sampling sequences within phyla, while OOD places all sequences from a genus wholly in training or wholly in testing. The same genome collection is partitioned differently, so ID scores do not establish performance on unseen genera.SourcesICCTax: a hierarchical taxonomic classifier for metagenomic sequences on a large language model · §3.1 Datasets; full-XML Fig.2 caption (a–c) |
| Uncertainty | The paper gives bootstrap confidence intervals for accuracy and average precision on both ID and OOD datasets.SourcesICCTax: a hierarchical taxonomic classifier for metagenomic sequences on a large language model · Results §§3.1–3.2.1; cached text lines 47–48, 51, 53–54; uncertainty/repeat-run/statistical-comparison passages |
| Entity type | Paper-specific computational evaluation protocol.SourcesICCTax: a hierarchical taxonomic classifier for metagenomic sequences on a large language model · Results §§3.1–3.2.1; cached text lines 47–48, 51, 53–54; uncertainty/repeat-run/statistical-comparison passages |
| Organisms | Major taxonomic groups in BERTax-derived ID/OOD datasets.SourcesICCTax: a hierarchical taxonomic classifier for metagenomic sequences on a large language model · Results §§3.1–3.2.1; cached text lines 47–48, 51, 53–54; uncertainty/repeat-run/statistical-comparison passages |
| Assays | Reference sequence/taxonomy annotations.SourcesICCTax: a hierarchical taxonomic classifier for metagenomic sequences on a large language model · Results §§3.1–3.2.1; cached text lines 47–48, 51, 53–54; uncertainty/repeat-run/statistical-comparison passages |
| Allowed inputs | DNA sequence fragments.SourcesICCTax: a hierarchical taxonomic classifier for metagenomic sequences on a large language model · Results §§3.1–3.2.1; cached text lines 47–48, 51, 53–54; uncertainty/repeat-run/statistical-comparison passages |
| Adaptation | Supervised hierarchical classification tested in in-distribution and out-of-distribution regimes.SourcesICCTax: a hierarchical taxonomic classifier for metagenomic sequences on a large language model · Results §§3.1–3.2.1; cached text lines 47–48, 51, 53–54; uncertainty/repeat-run/statistical-comparison passages |
How it works
How it worksComputational evaluation flow
Conceptual summary of the cited evaluation; exact task configuration and source version remain part of the protocol.
Sources
ICCTax: a hierarchical taxonomic classifier for metagenomic sequences on a large language model · Results §§3.1–3.2.1; cached text lines 47–48, 51, 53–54; uncertainty/repeat-run/statistical-comparison passagesEvaluation methodology
BERTax-derived ID, OOD and Complete datasets spanning major taxonomic groups. Accuracy and macro average precision at superkingdom and phylum levels; micro/macro AUC appears in supplements. MMseqs2, Minimap2, Kraken2, sourmash, MetaPhlAn4, CAT, DeepMicrobes and BERTax.
Sources
ICCTax: a hierarchical taxonomic classifier for metagenomic sequences on a large language model · Results §§3.1–3.2.1; cached text lines 47–48, 51, 53–54; uncertainty/repeat-run/statistical-comparison passages; Results §§3.1–3.2.1; cached text lines 47–48, 51, 53–54; uncertainty/repeat-run/statistical-comparison passages; Results §§3.1–3.2.1; cached text lines 47–48, 51, 53–54; uncertainty/repeat-run/statistical-comparison passagesRecorded evaluations
Each evaluation records what was tested and under which conditions.
Tested entities and results
Release 2026-09-17-d277315f7d76 · 2 evaluations · 2 metric rows. Different protocols are not a single leaderboard.
| Metric and finding | Coverage and uncertainty | Evidence |
|---|---|---|
| ICCTax: Hierarchical metagenomic taxonomy classification Configuration: ICCTaxTask: Hierarchical metagenomic taxonomy classificationDataset: ICCTax Complete dataset Macro average precision at genus rank on Complete dataset. Author-reported evaluation · Evaluation metadata: needs review | ||
| 67.20 Genus macro AveP Unit: % · Direction: unknown | Uncertainty: not reported in legacy extract Scored: Not reported · Eligible: Not reported | source checkedICCTax: a hierarchical taxonomic classifier for metagenomic sequences on a large language model · Table 2, ICCTax row, Genus column Source checking is not independent reproduction. |
| Kraken2: Hierarchical metagenomic taxonomy classification Configuration: Kraken2Task: Hierarchical metagenomic taxonomy classificationDataset: ICCTax Complete dataset Macro average precision at genus rank on Complete dataset. Independent external evaluation · Evaluation metadata: needs review | ||
| 70.56 Genus macro AveP Unit: % · Direction: unknown | Uncertainty: not reported in legacy extract Scored: Not reported · Eligible: Not reported | source checkedICCTax: a hierarchical taxonomic classifier for metagenomic sequences on a large language model · Table 2, Kraken2 row, Genus column Source checking is not independent reproduction. |
Papers and result coverage
Last literature check: 2026-09-17. Primary-paper discovery and source inspection. Source-checked results are not independently reproduced experiments.
| Paper or primary resource | Version | Reference |
|---|---|---|
| ICCTax: a hierarchical taxonomic classifier for metagenomic sequences on a large language model | version of record | Read source DOI: 10.1093/bioadv/vbaf257 |
What is still missing
- MetaPhlAn4 uses its official prebuilt database, unlike the common training database; preserve the asterisk.
- CAT and MetaPhlAn4 do not supply probabilities, so AveP is inapplicable, not zero.
- Bootstrap intervals exist in supplemental figures; main-table values lack printed intervals.
Search and extraction details
primary comparison table screened
Searches
- "PMC12619997"
Evidence locations
- Tables 1–2 and footnotes
- Section 3.2.1
- Supplement Figures S3–S4 (interval locations)
Strengths and limitations
Limitations and conditions
- ID and OOD scores use different taxonomic partition strategies. Reference-database coverage also varies between external comparison tools.
Sources
ICCTax: a hierarchical taxonomic classifier for metagenomic sequences on a large language model · Results §§3.1–3.2.1; cached text lines 47–48, 51, 53–54; uncertainty/repeat-run/statistical-comparison passages
Profile review details
Relevant full-paper computational evaluation sections, tables/captions and cited supplementary task passages were reviewed. Reporting omissions are scoped to the inspected sources. Original numerical results are unchanged.
Stable record: reported-task-f4b1c9373f0929Evidence table
Inspect claims, sources and review details
Trace each statement to its source and review. A context-only reference supports the record generally; it does not verify an individual field. Source checking does not reproduce an experiment.
One row per statement and cited source. Multiple citations are not independent evaluations. Shared locators are labelled explicitly.
17 evidence rows matching the loaded filters
| Property and statement | Original source and location | Review and provenance |
|---|---|---|
| Diagram caption Conceptual summary of the cited evaluation; exact task configuration and source version remain part of the protocol. Individual claims | ICCTax: a hierarchical taxonomic classifier for metagenomic sequences on a large language model Results §§3.1–3.2.1; cached text lines 47–48, 51, 53–54; uncertainty/repeat-run/statistical-comparison passages Version: version of record | source checked automated source review · 2026-09-16 Audit detailsRelevant full-paper computational evaluation sections, tables/captions and cited supplementary task passages were reviewed. Reporting omissions are scoped to the inspected sources. Original numerical results are unchanged. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Diagram steps ["Input: DNA sequence fragments.","Evaluation: Supervised hierarchical classification tested in in-distribution and out-of-distribution regimes.","Readout: Accuracy and macro average precision at superkingdom and phylum levels; micro/macro AUC appears in supplements."] Individual claims | ICCTax: a hierarchical taxonomic classifier for metagenomic sequences on a large language model Results §§3.1–3.2.1; cached text lines 47–48, 51, 53–54; uncertainty/repeat-run/statistical-comparison passages Version: version of record | source checked automated source review · 2026-09-16 Audit detailsRelevant full-paper computational evaluation sections, tables/captions and cited supplementary task passages were reviewed. Reporting omissions are scoped to the inspected sources. Original numerical results are unchanged. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Diagram title Computational evaluation flow Individual claims | ICCTax: a hierarchical taxonomic classifier for metagenomic sequences on a large language model Results §§3.1–3.2.1; cached text lines 47–48, 51, 53–54; uncertainty/repeat-run/statistical-comparison passages Version: version of record | source checked automated source review · 2026-09-16 Audit detailsRelevant full-paper computational evaluation sections, tables/captions and cited supplementary task passages were reviewed. Reporting omissions are scoped to the inspected sources. Original numerical results are unchanged. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Datasets BERTax-derived ID, OOD and Complete datasets spanning major taxonomic groups. Individual claims | ICCTax: a hierarchical taxonomic classifier for metagenomic sequences on a large language model Results §§3.1–3.2.1; cached text lines 47–48, 51, 53–54; uncertainty/repeat-run/statistical-comparison passages Version: version of record | source checked automated source review · 2026-09-16 Audit detailsRelevant full-paper computational evaluation sections, tables/captions and cited supplementary task passages were reviewed. Reporting omissions are scoped to the inspected sources. Original numerical results are unchanged. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Splits ID reserves 2,000 sequences per phylum for testing and uses the rest for training. OOD assigns every sequence in a genus to a single side of the train/test partition. The Complete-dataset classifier is a separate setting. Individual claims | ICCTax: a hierarchical taxonomic classifier for metagenomic sequences on a large language model §3.1 Datasets; Fig.2 caption (a–c) Version: version of record | source checked automated source review · 2026-09-16 Audit detailsRelevant full-paper computational evaluation sections, tables/captions and cited supplementary task passages were reviewed. Reporting omissions are scoped to the inspected sources. Original numerical results are unchanged. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Adaptation Supervised hierarchical classification tested in in-distribution and out-of-distribution regimes. Individual claims | ICCTax: a hierarchical taxonomic classifier for metagenomic sequences on a large language model Results §§3.1–3.2.1; cached text lines 47–48, 51, 53–54; uncertainty/repeat-run/statistical-comparison passages Version: version of record | source checked automated source review · 2026-09-16 Audit detailsRelevant full-paper computational evaluation sections, tables/captions and cited supplementary task passages were reviewed. Reporting omissions are scoped to the inspected sources. Original numerical results are unchanged. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Metrics Accuracy and macro average precision at superkingdom and phylum levels; micro/macro AUC appears in supplements. Individual claims | ICCTax: a hierarchical taxonomic classifier for metagenomic sequences on a large language model Results §§3.1–3.2.1; cached text lines 47–48, 51, 53–54; uncertainty/repeat-run/statistical-comparison passages Version: version of record | source checked automated source review · 2026-09-16 Audit detailsRelevant full-paper computational evaluation sections, tables/captions and cited supplementary task passages were reviewed. Reporting omissions are scoped to the inspected sources. Original numerical results are unchanged. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Baselines MMseqs2, Minimap2, Kraken2, sourmash, MetaPhlAn4, CAT, DeepMicrobes and BERTax. Individual claims | ICCTax: a hierarchical taxonomic classifier for metagenomic sequences on a large language model Results §§3.1–3.2.1; cached text lines 47–48, 51, 53–54; uncertainty/repeat-run/statistical-comparison passages Version: version of record | source checked automated source review · 2026-09-16 Audit detailsRelevant full-paper computational evaluation sections, tables/captions and cited supplementary task passages were reviewed. Reporting omissions are scoped to the inspected sources. Original numerical results are unchanged. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Leakage controls Figure 2 defines ID testing by sampling sequences within phyla, while OOD places all sequences from a genus wholly in training or wholly in testing. The same genome collection is partitioned differently, so ID scores do not establish performance on unseen genera. Individual claims | ICCTax: a hierarchical taxonomic classifier for metagenomic sequences on a large language model §3.1 Datasets; full-XML Fig.2 caption (a–c) Version: version of record | source checked automated source review · 2026-09-16 Audit detailsRelevant full-paper computational evaluation sections, tables/captions and cited supplementary task passages were reviewed. Reporting omissions are scoped to the inspected sources. Original numerical results are unchanged. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Uncertainty The paper gives bootstrap confidence intervals for accuracy and average precision on both ID and OOD datasets. Individual claims | ICCTax: a hierarchical taxonomic classifier for metagenomic sequences on a large language model Results §§3.1–3.2.1; cached text lines 47–48, 51, 53–54; uncertainty/repeat-run/statistical-comparison passages Version: version of record | source checked automated source review · 2026-09-16 Audit detailsRelevant full-paper computational evaluation sections, tables/captions and cited supplementary task passages were reviewed. Reporting omissions are scoped to the inspected sources. Original numerical results are unchanged. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
Sources and history
Release 2026-09-17-d277315f7d76 · Record review: needs review
2 source records and release history
- ICCTax: a hierarchical taxonomic classifier for metagenomic sequences on a large language model · Original source · version of record
- ICCTax: a hierarchical taxonomic classifier for metagenomic sequences on a large language model · Original source · version of record
Technical metadata and extraction receipts
Stable ID: reported-task-f4b1c9373f0929
- areas
- microbes-communities
- tasks
- Hierarchical metagenomic taxonomy classification
- entity level
- task
- version
- Not reported
- task
- Hierarchical metagenomic taxonomy classification
- scope note
- Paper-specific evaluation task; protocol completeness requires further extraction.
- benchmark research
- review date: 2026-09-17; status: primary_comparison_table_screened; primary sources: expansion-p3-icctax-2025; inspected locators: Tables 1–2 and footnotes; Section 3.2.1; Supplement Figures S3–S4 (interval locations); searched queries: "PMC12619997"; gaps: MetaPhlAn4 uses its official prebuilt database, unlike the common training database; preserve the asterisk.; CAT and MetaPhlAn4 do not supply probabilities, so AveP is inapplicable, not zero.; Bootstrap intervals exist in supplemental figures; main-table values lack printed intervals.; claim scope: Primary-paper discovery and source inspection. Source-checked results are not independently reproduced experiments.
- historical missing metadata
- protocol version: not_reported_in_legacy_extract; split: not_reported_in_legacy_extract
- 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 source-scoped record identifies the biological prediction task and holds its paper context. Preserve the existing task identity; exact split, model adaptation and scoring remain in linked evaluations or separate protocol records.; source ids: icctax-2025; source locator: Results §§3.1–3.2.1; cached text lines 47–48, 51, 53–54; uncertainty/repeat-run/statistical-comparison passages; 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.; Some legacy profile Entity type facts use the generic phrase computational evaluation protocol. That boilerplate is not sufficient to establish a single fixed protocol identity or to merge this task with another protocol record.