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Record W4309830272 · doi:10.1099/mgen.0.000891

Combining analytical epidemiology and genomic surveillance to identify risk factors associated with the spread of antimicrobial resistance in Salmonella enterica subsp. enterica serovar Heidelberg

2022· article· en· W4309830272 on OpenAlexafffund
Benjamin M. Hetman, David L. Pearl, Dillon Barker, James A. Robertson, John J. Nash, Richard J. Reid‐Smith, Agnes Agunos, Catherine D. Carrillo, Edward Topp, Gary Van Domselaar, E. Jane Parmley, Amrita Bharat, Michael R. Mulvey, Vanessa Allen, Eduardo N. Taboada

Bibliographic record

VenueMicrobial Genomics · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSalmonella and Campylobacter epidemiology
Canadian institutionsUniversity of TorontoPublic Health OntarioUniversity of GuelphAgriculture and Agri-Food CanadaCanadian Food Inspection AgencyWestern UniversityPublic Health Agency of Canada
FundersOntario Veterinary College, University of GuelphGenome Canada
KeywordsSalmonella entericaAntibiotic resistanceBiologyTransmission (telecommunications)SalmonellaAntimicrobialSerotypeMultiple drug resistanceLivestockMicrobiologyPlasmidBiotechnologyDrug resistanceAntibioticsBacteriaGeneGeneticsEcology

Abstract

fetched live from OpenAlex

Antimicrobial resistance (AMR) has become a critical threat to public health worldwide. The use of antimicrobials in food and livestock agriculture, including the production of poultry, is thought to contribute to the dissemination of antibiotic resistant bacteria (ARB) and the genes and plasmids that confer the resistant phenotype (ARG). However, the relative contribution of each of these processes to the emergence of resistant pathogens in poultry production and their potential role in the transmission of resistant pathogens in human infections, requires a deeper understanding of the dynamics of ARB and ARG in food production and the factors involved in the increased risk of transmission.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.033
GPT teacher head0.255
Teacher spread0.222 · 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 designObservational
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

Citations4
Published2022
Admission routes2
Has abstractyes

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