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Record W2754852449 · doi:10.1186/s12915-017-0423-1

Antibiotic resistance: it’s bad, but why isn’t it worse?

2017· review· en· W2754852449 on OpenAlexafffund
Nicholas Waglechner, Gerard D. Wright

Bibliographic record

VenueBMC Biology · 2017
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAntibiotic Resistance in Bacteria
Canadian institutionsMcMaster University
FundersCanadian Institutes of Health ResearchCanada Research Chairs
KeywordsBiologyAntibiotic resistanceAntibioticsBacteriaResistance (ecology)Drug resistanceMicrobiologyBiotechnologyEcologyGenetics

Abstract

fetched live from OpenAlex

Antibiotic natural products are ancient and so is resistance. Consequently, environmental bacteria harbor numerous and varied antibiotic resistance elements. Nevertheless, despite long histories of antibiotic production and exposure, environmental bacteria are not resistant to all known antibiotics. This means that there are barriers to the acquisition of a complete resistance armamentarium. The sources, distribution, and movement of resistance mechanisms in different microbes and bacterial populations are mosaic features that act as barriers to slow this movement, thus moderating the emergence of bacterial pan-resistance. This is highly relevant to understanding the emergence of resistance in pathogenic bacteria that can inform better antibiotic management practices and influence new drug discovery.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.338
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0020.001
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 head0.085
GPT teacher head0.374
Teacher spread0.289 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations91
Published2017
Admission routes2
Has abstractyes

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