sourmash (Final model dataset, Mock et al. 2022)
Configuration of sourmash: sourmash with default parameters; reference built from the dataset's training partition.
Overview
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Consult the linked sources for architecture or protocol details. Missing evidence is not evidence of a missing capability.
Evaluations and results
1 evaluation · 3 results. Different protocols are not a single leaderboard.
Protocol: BERTax Final model dataset: taxonomic classification at superkingdom and phylum, and genus (Mock et al. 2022) · Dataset: BERTax Final model dataset (Mock et al. 2022)
Sorted by Macro average precision (genus rank; macro average over classes; unclassified samples treated as random guesses) (higher is better). The best value in each column is highlighted. Decimals are rounded for display; each value links to the printed value and its source.
| Tested configuration | Macro average precision (genus rank; macro average over classes; unclassified samples treated as random guesses) | Macro average precision (phylum rank; macro average over classes; unclassified samples treated as random guesses) | Macro average precision (superkingdom rank; macro average over classes; unclassified samples treated as random guesses) |
|---|---|---|---|
| sourmash (Final model dataset, Mock et al. 2022)Evaluation | 3.07% | 8% | 31% |
All 3 result rows with coverage, uncertainty and sources
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Strengths, limitations and unresolved questions
Evidence
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Sources and history
Release 2026-10-22-1b7cf027e4f6 · Record review: source checked
1 source record and release history
- Taxonomic classification of DNA sequences beyond sequence similarity using deep neural networks · Original source · PNAS 119(35):e2122636119, published 2022-08-26; PMC9436379 full-text XML
Technical metadata and extraction receipts
Stable ID: dna-pathogen-20261012-bertax-config-sourmash-final
- areas
- microbes-communities
- contexts
- research
- method types
- conventional_pipeline
- reported name
- sourmash
- foundation model eligible
- false
- parameters
- sourmash with default parameters; reference built from the dataset's training partition
- source locator
- Materials and Methods 'Comparison to Other Tools' paragraphs 1-2; Results 'Comparison to Other Classification Approaches' paragraphs 1 and 3
- access reuse
- Tool run by the article's authors; trained models and reference builds are not released except the downloadable final BERTax model
- missing metadata
- version: reason: unreported; note: Tool versions are not stated in the article or SI, except MMseqs2-linclust 11.e1a1c used for dataset clustering
- review
- method: source-hash-verification; scope-review; reviewer: claude; reviewer note: Separate Claude review agent (rev-mlvb), independent of the extracting agent; no human review claimed; date: 2026-10-12; artifact sha256: c0bb2f1bba8680b300076292389b39346ae58bea4bdf28647dd9462333b3bcd7; retrieval url: https://www.ebi.ac.uk/europepmc/webservices/rest/PMC9436379/fullTextXML; note: Checked against the re-downloaded article XML (SHA-256 matches) and SI.
- provenance note
- sourmash as run by the BERTax authors with the final dataset's training partition as reference or training data.
Related records
- configuration of: sourmash
- system: sourmash on the BERTax final model dataset