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Record W4388734403 · doi:10.1128/spectrum.02744-23

A standardized nomenclature for resistance-modifying agents in the Comprehensive Antibiotic Resistance Database

2023· article· en· W4388734403 on OpenAlexafffund
Keaton W Smith, Brian Alcock, Shawn French, Maya A. Farha, Amogelang R. Raphenya, Eric D. Brown, Andrew G. McArthur

Bibliographic record

VenueMicrobiology Spectrum · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAntibiotic Resistance in Bacteria
Canadian institutionsMcMaster University
FundersCanadian Institutes of Health ResearchGovernment of Canada
KeywordsAntibiotic resistanceNomenclatureAntibioticsResistance (ecology)BiologyComputational biologyDatabaseMedicineComputer scienceGeneticsTaxonomy (biology)ZoologyEcology

Abstract

fetched live from OpenAlex

ABSTRACT Resistance-modifying agents have been historically underrepresented in the Comprehensive Antibiotic Resistance Database (CARD). In the process of curating over 60 new molecules into CARD’s Antibiotic Resistance Ontology, the limitations of current classification and nomenclature for these molecules were addressed. We propose a new standardized nomenclature based on mechanism of action, including inhibitors of antibiotic resistance mechanisms, adjuvants enhancing antibiotic entry, adjuvants inhibiting antibiotic removal, adjuvants that alter cell physiology, and host-related antibiotic adjuvants. IMPORTANCE While increasing rates of antimicrobial resistance undermine our current arsenal of antibiotics, resistance-modifying agents (RMAs) hold promise to extend the lifetime of these important molecules. We here provide a standardized nomenclature for RMAs within the Comprehensive Antibiotic Resistance Database in aid of RMA discovery, data curation, and genome mining.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.796
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.028
GPT teacher head0.302
Teacher spread0.274 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations16
Published2023
Admission routes2
Has abstractyes

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