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Record W2333333711 · doi:10.3201/eid2205.151584

Expansion of Shiga Toxin–Producing<i>Escherichia coli</i>by Use of Bovine Antibiotic Growth Promoters

2016· article· en· W2333333711 on OpenAlexafffund
Jong-Chul Kim, Linda Chui, Yang Wang, Jianzhong Shen, Byeonghwa Jeon

Bibliographic record

VenueEmerging infectious diseases · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEscherichia coli research studies
Canadian institutionsUniversity of Alberta
FundersNational Key Research and Development Program of ChinaKorea National Institute of HealthNatural Sciences and Engineering Research Council of CanadaNational Institutes of HealthUniversity of Alberta
KeywordsEscherichia coliMicrobiologyAntibioticsPromoterShiga toxinBiologyEscherichia coli ProteinsToxinVirologyGeneticsGene

Abstract

fetched live from OpenAlex

Antibiotics are routinely used in food-producing animals to promote growth and prevent infectious diseases. We investigated the effects of bovine antibiotic growth promoters (bAGPs) on the propagation and spread of Shiga toxin (Stx)-encoding phages in Escherichia coli. Co-culture of E. coli O157:H7 and other E. coli isolated from cattle in the presence of sublethal concentrations of bAGPs significantly increased the emergence of non-O157, Stx-producing E. coli by triggering the SOS response system in E. coli O157:H7. The most substantial mediation of Stx phage transmission was induced by oxytetracyline and chlortetracycline, which are commonly used in agriculture. bAGPs may therefore contribute to the expansion of pathogenic Stx-producing E. coli.

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.010
GPT teacher head0.249
Teacher spread0.239 · 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

Citations19
Published2016
Admission routes2
Has abstractyes

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