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Record W2074780061 · doi:10.1111/jvp.12104

Workshop report: The 2012 Antimicrobial Agents in Veterinary Medicine: exploring the consequences of antimicrobial drug use: a 3<scp>‐D</scp> approach

2014· article· en· W2074780061 on OpenAlexaff
Marilyn N. Martinez, Joseph M. Blondeau, Carl E. Cerniglia, Johanna Fink‐Gremmels, Sebastian Guenther, Robert P. Hunter, Xuli Li, Mark G. Papich, P. Silley, S. Soback, Pierre‐Louis Toutain, Q. Zhang

Bibliographic record

VenueJournal of Veterinary Pharmacology and Therapeutics · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAntibiotic Resistance in Bacteria
Canadian institutionsHealth CanadaUniversity of Saskatchewan
FundersWorld Health Organization
KeywordsVeterinary drugAntimicrobialVeterinary DrugsResilience (materials science)Animal healthAntibiotic resistanceBiotechnologyMedicineVeterinary medicineBusinessBiologyMicrobiologyAntibiotics

Abstract

fetched live from OpenAlex

Antimicrobial resistance is a global challenge that impacts both human and veterinary health care. The resilience of microbes is reflected in their ability to adapt and survive in spite of our best efforts to constrain their infectious capabilities. As science advances, many of the mechanisms for microbial survival and resistance element transfer have been identified. During the 2012 meeting of Antimicrobial Agents in Veterinary Medicine (AAVM), experts provided insights on such issues as use vs. resistance, the available tools for supporting appropriate drug use, the importance of meeting the therapeutic needs within the domestic animal health care, and the requirements associated with food safety and food security. This report aims to provide a summary of the presentations and discussions occurring during the 2012 AAVM with the goal of stimulating future discussions and enhancing the opportunity to establish creative and sustainable solutions that will guarantee the availability of an effective therapeutic arsenal for veterinary species.

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 score0.707
Threshold uncertainty score0.521

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.084
GPT teacher head0.341
Teacher spread0.257 · 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

Citations12
Published2014
Admission routes1
Has abstractyes

Explore more

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