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Record W2769044472 · doi:10.1017/s1466252317000147

Phage-mediated dissemination of virulence factors in pathogenic bacteria facilitated by antibiotic growth promoters in animals: a perspective

2017· review· en· W2769044472 on OpenAlexafffund
Migma Dorji Tamang, Hoon H. Sunwoo, Byeonghwa Jeon

Bibliographic record

VenueAnimal Health Research Reviews · 2017
Typereview
Languageen
FieldEnvironmental Science
TopicBacteriophages and microbial interactions
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsVirulenceMicrobiologyAntibioticsPerspective (graphical)BacteriaBiologyPathogenic bacteriaVirulence factorGeneticsGeneComputer science

Abstract

fetched live from OpenAlex

Addition of sub-therapeutic antibiotics to the feed of food-producing animals for growth promotion and disease prevention has become a common agricultural practice in many countries. The emergence of antibiotic-resistant pathogens is a looming concern associated with the use of antibiotic growth promoters (AGPs) around the world. In addition, some studies have shown that AGPs may not only affect antibiotic resistance but may also stimulate the dissemination of virulence factors via bacteriophages. Although only a few studies are currently available in the literature regarding this topic, in this article we endeavor to provide a perspective about how AGPs would impact the transmission of virulence factors by horizontal gene transfer via phages in a few pathogenic bacterial species significant to livestock production.

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)0.005
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.918
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.261
GPT teacher head0.513
Teacher spread0.252 · 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

Citations18
Published2017
Admission routes2
Has abstractyes

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