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Record W4383422100 · doi:10.7554/elife.83107.sa2

Author response: The evolutionary mechanism of non-carbapenemase carbapenem-resistant phenotypes in Klebsiella spp

2023· peer-review· en· W4383422100 on OpenAlexaff
Natalia C. Rosas, Jonathan J. Wilksch, Jake Barber, Jiahui Li, Yanan Wang, Zhewei Sun, Andrea Rocker, Chaille T. Webb, Laura Perlaza-Jiménez, Christopher J. Stubenrauch, Dhanasekaran Vijaykrishna, Jiangning Song, George Taiaroa, Mark R. Davies, Richard A. Strugnell, Qiyu Bao, Tieli Zhou, Michael J. McDonald, Trevor Lithgow

Bibliographic record

Venuenot available
Typepeer-review
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAntibiotic Resistance in Bacteria
Canadian institutionsImpact
Fundersnot available
KeywordsCarbapenemBiologyLocus (genetics)GeneticsPhenotypeGeneAntibiotic resistanceSelection (genetic algorithm)ImipenemAntibioticsMachine learning

Abstract

fetched live from OpenAlex

Genetic, phenotypic, and evolutionary analysis of a clinical isolate provides an explanation of the intricate genetic factors behind non-carbapenemase carbapenem resistance and sheds light on the evolutionary mechanisms that influence the molecular basis of antibiotic resistance.

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 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)0.006
metaresearch head score (Gemma)0.073
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.192
Threshold uncertainty score0.641

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.073
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0070.005
Insufficient payload (model declined to judge)0.1920.079

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 head0.022
GPT teacher head0.305
Teacher spread0.283 · 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
GenreCommentary

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

Citations1
Published2023
Admission routes1
Has abstractyes

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