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Record W2341841777 · doi:10.1080/03079457.2016.1141345

Experimental reproduction of necrotic enteritis in chickens: a review

2016· review· en· W2341841777 on OpenAlexafffund
John F. Prescott, Joan A. Smyth, Bahram Shojadoost, Andrew R Vince

Bibliographic record

VenueAvian Pathology · 2016
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsUniversity of Guelph
FundersAgriculture and Agri-Food CanadaOntario Ministry of Agriculture, Food and Rural AffairsCanadian Poultry Research CouncilUniversity of ConnecticutInvest Northern IrelandU.S. Department of Agriculture
KeywordsClostridium perfringensBiologyCoccidiaVirulenceMicrobiologyViable but nonculturableEnteritisDiseaseCoccidiosisVeterinary medicineBacteriaGeneInternal medicineMedicineGenetics

Abstract

fetched live from OpenAlex

This review discusses key factors important in successful experimental reproduction of necrotic enteritis (NE) in chickens, and how these factors can be adjusted to affect the severity of the lesions induced. The critical bacterial factor is the need to use virulent, netB-positive, strains of Clostridium perfringens; disease severity can be enhanced by using netB-positive C. perfringens strains that are also tpeL-positive, by the use of young rather than old broth cultures, and by the number of days of inoculation and the number of bacteria used. Use of cereals rich in non-starch polysaccharides can enhance disease, as does use of animal proteins. Administration of coccidia, including coccidial vaccines, combined with netB-positive C. perfringens, increases the severity of experimentally-induced NE. Dietary manipulation may be less important in coccidia-based models since the latter are so effective. Disease scoring systems and welfare considerations are discussed.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.051
GPT teacher head0.313
Teacher spread0.263 · 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

Citations99
Published2016
Admission routes2
Has abstractyes

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