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Record W4414222406 · doi:10.1038/s44259-025-00154-8

Towards the integration of antibiotic resistance gene mobility into environmental surveillance and risk assessment

2025· review· en· W4414222406 on OpenAlexaff
Uli Klümper, Peiju Fang, Bing Li, Yu Xia, Dominic Frigon, Kerry A. Hamilton, Hunter Quon, Thomas U. Berendonk, Magali de la Cruz Barron

Bibliographic record

Venuenpj Antimicrobials and Resistance · 2025
Typereview
Languageen
FieldEnvironmental Science
TopicPharmaceutical and Antibiotic Environmental Impacts
Canadian institutionsMcGill University
FundersNational Key Research and Development Program of ChinaChina Scholarship CouncilJoint Programming Initiative on Antimicrobial Resistance
KeywordsRisk assessmentAntibiotic resistanceLimitingEnvironmental risk assessmentResistance (ecology)Human healthAntibioticsRisk factor

Abstract

fetched live from OpenAlex

Antibiotic resistance gene (ARG) mobility plays a crucial role in the spread of antimicrobial resistance across One Health settings. Current environmental surveillance often overlooks the significance of ARG mobility, limiting risk assessment accuracy. This perspective highlights that with recent methodological advances in detecting ARG mobility, relevant databases, and improved quantitative microbial risk assessment frameworks, the time to integrate ARG mobility into environmental antimicrobial resistance (AMR) surveillance and risk assessment is now.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.937
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.024
GPT teacher head0.314
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 designOther design
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

Citations21
Published2025
Admission routes1
Has abstractyes

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