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Record W7046409475

Current use of antibiotic growth promoters and alternative products

2007· article· en· W7046409475 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsFeed additiveProduction (economics)Consumer demandFood processingAntibioticsAnimal feedHealth benefitsFood productsAnimal production
DOInot available

Abstract

fetched live from OpenAlex

Antibiotics are used at sub-therapeutic levels for growth promotion of livestock. Concerns about the development of antibiotic-resistant bacteria and a growing consumer demand for 'green food products' have made this practice less acceptable than in the past. Antibiotic growth promoters (AGPs) are now banned in the European Union. However, AGPs are still an integral part of animal production in Australia, Canada, USA and other countries. In the past decade, much research has been conducted on alternative feed additives and feeding strategies. The majority of these alternatives are aimed at enhancing gastrointestinal function through manipulation of gut microflora. Strategies for improving growth performance and health of pigs and poultry by dietary manipulation include optimizing the physical form of the feed through grain and feed processing and enhancing feed digestibility and nutrient availability by exogenous enzyme supplementation. Other strategies include liquid feeding and natural dietary additives such as organic acids, probiotics, prebiotics, plant extracts and essential oils. In this article, we review the history and present use of AGPs in pig and poultry production and discuss feeding strategies and feed additives that have been suggested as replacements for AGPs.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.003

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 head0.029
GPT teacher head0.289
Teacher spread0.261 · 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

Citations0
Published2007
Admission routes1
Has abstractyes

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