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Record W2606702867 · doi:10.1186/s13567-017-0425-6

Antimicrobial potential of bacteriocins in poultry and swine production

2017· review· en· W2606702867 on OpenAlexafffund
Amel Ben Lagha, B. Haas, Marcelo Gottschalk, Daniel Grenier

Bibliographic record

VenueVeterinary Research · 2017
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicProbiotics and Fermented Foods
Canadian institutionsCentre de Recherche Industrielle du QuébecUniversité de MontréalFonds de Recherche du Québec - SantéUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaMinistère de l'Agriculture, des Pêcheries et de l'Alimentation
KeywordsBacteriocinAntimicrobialAntibioticsAntibiotic resistanceBiotechnologyAnimal productionLivestockAnimal healthBiologyPoultry farmingAntimicrobial peptidesVeterinary medicineMicrobiologyMedicine

Abstract

fetched live from OpenAlex

The routine use of antibiotics in agriculture has contributed to an increase in drug-resistant bacterial pathogens in animals that can potentially be transmitted to humans. In 2000, the World Health Organization identified resistance to antibiotics as one of the most significant global threats to public health and recommended that the use of antibiotics as additives in animal feed be phased out or terminated, particularly those used to treat human infections. Research is currently being carried out to identify alternative antimicrobial compounds for use in animal production. A number of studies, mostly in vitro, have provided evidence indicating that bacteriocins, which are antimicrobial peptides of bacterial origin, may be promising alternatives to conventional antibiotics in poultry and swine production. This review provides an update on bacteriocins and their potential for use in the poultry and swine industries.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.411
GPT teacher head0.457
Teacher spread0.046 · 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 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

Citations174
Published2017
Admission routes2
Has abstractyes

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