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Record W4405884653 · doi:10.1016/j.psj.2024.104663

Coccidiosis in poultry: Disease mechanisms, control strategies, and future directions

2024· review· en· W4405884653 on OpenAlexaff
Greg F. Mathis, Brett Lumpkins, Hector M. Cervantes, Steve Fitz-Coy, Mark C. Jenkins, Matthew Jones, K.R. Price, Rami A. Dalloul

Bibliographic record

VenuePoultry Science · 2024
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicCoccidia and coccidiosis research
Canadian institutionsMantech (Canada)
Fundersnot available
KeywordsCoccidiosisDisease controlDiseaseVeterinary medicineControl (management)BusinessBiologyBiotechnologyMedicineComputer sciencePathologyArtificial intelligence

Abstract

fetched live from OpenAlex

According to a 2023 survey by the American Veterinarians in Broiler Production, Coccidiosis is the number one disease in the broiler poultry industry. Coccidiosis results in the reduction of growth rate, decrease in feed efficiency, and poor body weight uniformity. The more we understand this disease the more we can move forward towards control. The purpose of this symposium was to increase our understanding of coccidiosis. The topics discussed were the diagnosis, immune response, control/prevention, medications (natural and chemical), and interactions with other diseases. The Coccidiosis Symposium provided up to date information from both research and field experiences. This information will be useful for production managers, nutritionists, and veterinarians, as well as providing opportunities for future research.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.995
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.004
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.029
GPT teacher head0.310
Teacher spread0.281 · 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 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

Citations37
Published2024
Admission routes1
Has abstractyes

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