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Record W4398242664 · doi:10.1016/j.psj.2024.103801

Review: Myopathies in broilers: supply chain approach to provide solutions to challenges related to raising fast growing birds

2024· review· en· W4398242664 on OpenAlexaff
Shai Barbut, Randolph Mitchell, Parker Hall, Craig Bacon, Ritchard Bailey, C.M. Owens, Massimiliano Petracci

Bibliographic record

VenuePoultry Science · 2024
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsUniversity of Guelph
FundersPoultry Science Association
KeywordsRaising (metalworking)Supply chainBusinessEnvironmental scienceMathematics

Abstract

fetched live from OpenAlex

This review is a summary of a Poultry Science Association symposium addressing myopathies in broilers' breast meat, focusing on the interactions between genetics, nutrition, husbandry, and meat processing. The Pectoralis major myopathies (woody breast [WB]; white striping [WS]; spaghetti meat [SM]) and Pectoralis minor ("feathering") are described, followed by discussing their prevalence, potential causes, current and future ways to mitigate, as well as detection methods (in live birds and meat) as well as ways to utilize affected meat. Overall, breast myopathies remain an important focus across the poultry industry and whilst a lot of data and knowledge has been gathered, it is clear that there is still a lot to understand. As there are multiple factors impacting the occurrence of breast myopathies, their reduction relies on a holistic approach. Ongoing balanced breeding strategies by poultry breeders is targeting the longer-term genetic component but comprehending the significant influence from nongenetic factors (short-term solutions such as nutrition) remains a key area of opportunity. Consequently, understanding the physiology and biological needs of the muscle through the life of the bird is critical to reduce the myopathies (e.g., minimizing oxidative stress) and gain more insight into their etiology.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.985
Threshold uncertainty score0.923

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.005
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.110
GPT teacher head0.327
Teacher spread0.216 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations32
Published2024
Admission routes1
Has abstractyes

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