comparison_design: dna-pathogen-20261012-metagenn-benchmark
Comparison design of MetageNN long-read genus classification benchmark (Peres da Silva et al. 2024)
All baselines used the same database of genomes; the non-machine-learning tools' indices were built from the main-database genomes and run with default settings.
Descriptive fact transcribed from the pinned source.
Evidence
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Evidence table
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6 evidence rows matching the loaded filters
| Property and statement | Original source and location | Review and provenance |
|---|---|---|
| Claimed field comparison_design Context-only references | MetageNN: a memory-efficient neural network taxonomic classifier robust to sequencing errors and missing genomes Methods 'Databases' paragraph 1; Results 'MetageNN presents a higher F1 score than GeNet...' Experimental setup Version: BMC Bioinformatics 25:153, published 2024-04-16; PMC11022314 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Source location Methods 'Databases' paragraph 1; Results 'MetageNN presents a higher F1 score than GeNet...' Experimental setup Context-only references | MetageNN: a memory-efficient neural network taxonomic classifier robust to sequencing errors and missing genomes Methods 'Databases' paragraph 1; Results 'MetageNN presents a higher F1 score than GeNet...' Experimental setup Version: BMC Bioinformatics 25:153, published 2024-04-16; PMC11022314 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Claimed value All baselines used the same database of genomes; the non-machine-learning tools' indices were built from the main-database genomes and run with default settings. Context-only references | MetageNN: a memory-efficient neural network taxonomic classifier robust to sequencing errors and missing genomes Methods 'Databases' paragraph 1; Results 'MetageNN presents a higher F1 score than GeNet...' Experimental setup Version: BMC Bioinformatics 25:153, published 2024-04-16; PMC11022314 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Description Descriptive fact transcribed from the pinned source. Context-only references | MetageNN: a memory-efficient neural network taxonomic classifier robust to sequencing errors and missing genomes Methods 'Databases' paragraph 1; Results 'MetageNN presents a higher F1 score than GeNet...' Experimental setup Version: BMC Bioinformatics 25:153, published 2024-04-16; PMC11022314 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Relationship: subject dna-pathogen-20261012-metagenn-benchmark Context-only references | MetageNN: a memory-efficient neural network taxonomic classifier robust to sequencing errors and missing genomes Methods 'Databases' paragraph 1; Results 'MetageNN presents a higher F1 score than GeNet...' Experimental setup Version: BMC Bioinformatics 25:153, published 2024-04-16; PMC11022314 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Name comparison_design: dna-pathogen-20261012-metagenn-benchmark Context-only references | MetageNN: a memory-efficient neural network taxonomic classifier robust to sequencing errors and missing genomes Methods 'Databases' paragraph 1; Results 'MetageNN presents a higher F1 score than GeNet...' Experimental setup Version: BMC Bioinformatics 25:153, published 2024-04-16; PMC11022314 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
Sources and history
Release 2026-10-22-1b7cf027e4f6 · Record review: source checked
1 source record and release history
- MetageNN: a memory-efficient neural network taxonomic classifier robust to sequencing errors and missing genomes · Original source · BMC Bioinformatics 25:153, published 2024-04-16; PMC11022314 full-text XML
Technical metadata and extraction receipts
Stable ID: dna-pathogen-20261012-metagenn-claim-same-database
- field
- comparison_design
- value
- All baselines used the same database of genomes; the non-machine-learning tools' indices were built from the main-database genomes and run with default settings.
- source locator
- Methods 'Databases' paragraph 1; Results 'MetageNN presents a higher F1 score than GeNet...' Experimental setup
- review
- method: source-hash-verification; independent-cell-check; 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: ac4b137508d295d5e04e9be263bef40bdcf2c494e096407e5896611194f14e12; retrieval url: https://www.ebi.ac.uk/europepmc/webservices/rest/PMC11022314/fullTextXML; method note: Transcribed from the pinned article XML text.; note: Checked against the re-downloaded article XML (SHA-256 matches).