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Record W2795908627 · doi:10.1016/j.aninu.2018.03.002

Use of antibiotics in broiler production: Global impacts and alternatives

2018· review· en· W2795908627 on OpenAlexafffund
Youcef Mehdi, Marie-Pierre Létourneau-Montminy, Marie‐Lou Gaucher, Younès Chorfi, Gayatri Suresh, Tarek Rouissi, Satinder Kaur Brar, Caroline Côté, Antonio Avalos Ramírez, Stéphane Godbout

Bibliographic record

VenueAnimal nutrition · 2018
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsCentre National en Électrochimie et en Technologies EnvironnementalesCollège ShawiniganInstitut National de la Recherche ScientifiqueUniversité de MontréalUniversité LavalInstitut de Recherche et de Développement en Agroenvironnement
FundersMitacs
KeywordsAntibioticsBroilerBiotechnologyBiologyBacteriocinHuman healthBacteriaFeed additiveAnimal productionProduction (economics)Yield (engineering)Food scienceAntimicrobialMicrobiologyAnimal scienceMedicineEnvironmental healthEconomics

Abstract

fetched live from OpenAlex

Antibiotics are used to fight bacterial infections. However, a selective pressure gave rise to bacteria resistant to antibiotics. This leaves scientists worried about the danger to human and animal health. Some strategies can be borrowed to reduce the use of antibiotics in chicken farms. Much research has been carried out to look for natural agents with similar beneficial effects of growth promoters. The aim of these alternatives is to maintain a low mortality rate, a good level of animal yield while preserving environment and consumer health. Among these, the most popular are probiotics, prebiotics, enzymes, organic acids, immunostimulants, bacteriocins, bacteriophages, phytogenic feed additives, phytoncides, nanoparticles and essential oils.

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)0.001
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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.124
GPT teacher head0.334
Teacher spread0.210 · 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

Citations654
Published2018
Admission routes2
Has abstractyes

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