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Record W2109558197 · doi:10.1093/femsle/fnv122

Prebiotics and gut microbiota in chickens

2015· review· en· W2109558197 on OpenAlexaff
Mohsen Pourabedin, Xin Zhao

Bibliographic record

VenueFEMS Microbiology Letters · 2015
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsMcGill University
Fundersnot available
KeywordsPrebioticGut floraBiologyImmune systemInulinHealth benefitsLactobacillusProbioticBiotechnologyMicrobiologyFood scienceImmunologyBacteriaMedicineGeneticsFermentation

Abstract

fetched live from OpenAlex

Prebiotics are non-digestible feed ingredients that are metabolized by specific members of intestinal microbiota and provide health benefits for the host. Fermentable oligosaccharides are best known prebiotics that have received increasing attention in poultry production. They act through diverse mechanisms, such as providing nutrients, preventing pathogen adhesion to host cells, interacting with host immune systems and affecting gut morphological structure, all presumably through modulation of intestinal microbiota. Currently, fructooligosaccharides, inulin and mannanoligosaccharides have shown promising results while other prebiotic candidates such as xylooligosaccharides are still at an early development stage. Despite a growing body of evidence reporting health benefits of prebiotics in chickens, very limited studies have been conducted to directly link health improvements to prebiotic-dependent changes in the gut microbiota. This article visits the current knowledge of the chicken gastrointestinal microbiota and reviews most recent publications related to the roles played by prebiotics in modulation of the gut microbiota and immune functions. Progress in this field will help us better understand how the gut microbiota contributes to poultry health and productivity, and support the development of new prebiotic products as an alternative to in-feed antibiotics.

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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
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 head0.048
GPT teacher head0.268
Teacher spread0.220 · 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

Citations284
Published2015
Admission routes1
Has abstractyes

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