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Record W4417066629 · doi:10.1016/j.psj.2025.106200

The inflammation saga: Breakthrough nutritional insights for poultry

2025· article· en· W4417066629 on OpenAlexaff
Rajesh Jha, D.R. Korver, Woo Kyun Kim, Leon Marchal, Kirsty Gibbs, J. Halley

Bibliographic record

VenuePoultry Science · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsInflammationImmune systemContext (archaeology)Gut floraSustainabilityMucosal inflammationAnimal healthPsychological intervention

Abstract

fetched live from OpenAlex

This symposium offered a broader perspective on gut inflammation in poultry. It covered the basic factors that trigger gut inflammation, their potential effects on performance and health, and potential strategies to manage inflammation and mitigate its adverse effects. Gut inflammation has emerged as a critical determinant of health and performance in modern poultry production. A focus was placed on key nutritional factors that influence gut integrity and immune activation, including the pro- and anti-inflammatory roles of specific feed components. The role of dietary fiber was discussed, emphasizing its dualistic effects as both a potential irritant and a modulator of microbial populations and immune functions. The symposium also discussed the roles of amino acids such as threonine, glutamine, and arginine, which aid in mucosal repair and immune modulation. Additionally, the impact of mineral chelation, particularly involving zinc and copper, was discussed in the context of enhancing mineral bioavailability while minimizing oxidative and inflammatory responses. Ultimately, the symposium explored a practical approach to managing gut inflammation in commercial poultry operations, emphasizing the importance of early nutritional interventions and targeted feed formulation and nutrition strategies. Lastly, it was concluded that by integrating current scientific evidence with practical insights, poultry nutritionists and industry stakeholders should employ multiple approaches based on actionable knowledge to optimize gut health and reduce inflammation, thereby enhancing bird performance and the sustainability of the poultry industry.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.779
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.016
GPT teacher head0.254
Teacher spread0.237 · 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 designBench or experimental
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

Citations1
Published2025
Admission routes1
Has abstractyes

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