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Record W4306871314 · doi:10.1093/nar/gkac920

CARD 2023: expanded curation, support for machine learning, and resistome prediction at the Comprehensive Antibiotic Resistance Database

2022· article· en· W4306871314 on OpenAlexafffund
Brian Alcock, William Huynh, Romeo Chalil, Keaton W Smith, Amogelang R. Raphenya, Mateusz A. Wlodarski, Arman Edalatmand, Aaron Petkau, Sohaib A Syed, Kara K. Tsang, Sheridan J.C. Baker, Mugdha Dave, Madeline C. McCarthy, Karyn M Mukiri, Jalees A. Nasir, Bahar Golbon, Hamna Imtiaz, Xingjian Jiang, Komal Kaur, Megan W‐L Kwong, Zi Cheng Liang, Keyu C Niu, Prabakar Shan, Jasmine Y J Yang, Kristen L. Gray, Gemma R Hoad, Baofeng Jia, Timsy Bhando, Lindsey A. Carfrae, Maya A. Farha, Shawn French, Rodion Gordzevich, Kenneth Rachwalski, Megan M. Tu, Emily Bordeleau, Damion Dooley, Emma Griffiths, Haley L. Zubyk, Eric D. Brown, Finlay Maguire, Robert G. Beiko, William Hsiao, Fiona S. L. Brinkman, Gary Van Domselaar, Andrew G. McArthur

Bibliographic record

VenueNucleic Acids Research · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAntibiotic Resistance in Bacteria
Canadian institutionsDalhousie UniversitySimon Fraser UniversityUniversity of ManitobaPublic Health Agency of CanadaMcMaster University
FundersMichael G. DeGroote Institute for Infectious Disease Research, McMaster UniversityCisco Systems CanadaSimon Fraser UniversityInstitute of Infection and ImmunityCanadian Institutes of Health ResearchGenome CanadaCanada Foundation for InnovationNatural Sciences and Engineering Research Council of CanadaMcMaster UniversityCisco Systems
KeywordsResistomeBiologyDatabaseData curationGeneticsComputational biologyAntibiotic resistanceGene nomenclatureGenomeGeneAntibioticsComputer scienceMobile genetic elementsWorld Wide Web

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.056
Threshold uncertainty score0.187

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.020
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0070.008
Science and technology studies0.0020.001
Scholarly communication0.0050.004
Open science0.0070.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0560.036

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.033
GPT teacher head0.332
Teacher spread0.299 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2,141
Published2022
Admission routes2
Has abstractyes

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