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Record W2089325460 · doi:10.2527/jas.2011-3912

HORSE SPECIES SYMPOSIUM: Pathogenic and reproductive dysfunction in horses1

2011· article· en· W2089325460 on OpenAlexaboutno aff
Peter L. Ryan

Bibliographic record

VenueJournal of Animal Science · 2011
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Equine Medical Research
Canadian institutionsnot available
Fundersnot available
KeywordsEndometritisPathogenBiologyVeterinary medicinePregnancyMedicineImmunology

Abstract

fetched live from OpenAlex

One of the major factors contributing to production losses in the equine industry is pathogen-associated reproductive dysfunction. Although it is difficult to place a true value on the economic losses associated with pathogen-induced reproductive dysfunction in the horse due to the varying value of individual animals, the financial loss and emotional stress to horse owners and breeders is significant. Pathogenic organisms are associated with poor reproductive performance in the stallion and open and pregnant mare. Thus, the Horse Species Symposium held in Denver, Colorado, on July 15, 2010, at the joint meeting of the American Society of Animal Science, American Society of Dairy Science, Poultry Science Association, Asociación Mexicana de Producción Animal, and Canadian Society of Animal Science targeted 3 areas of significance: 1) contagious equine metritis (CEM), 2) identification of pathogens associated with endometritis and chronic endometritis in the mare, and 3) pathogen invasion during uterine infection leading to premature birth in mares. The primary goal of the symposium was to give an update on these 3 areas of concern and what progress has been made in the management, diagnosis, and treatment of these infectious conditions that have led to reduced reproductive performance in the equine species.

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)0.002
metaresearch head score (Gemma)0.002
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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0050.002
Insufficient payload (model declined to judge)0.0090.002

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 head0.136
GPT teacher head0.357
Teacher spread0.221 · 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
GenreOther

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

Citations0
Published2011
Admission routes1
Has abstractyes

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