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Selective breeding of Arabian and Thoroughbred racehorses in Algeria: perceptions, objectives and practices of owners-breeders

2014· article· en· W2099263475 on OpenAlex
Safia Tennah, Frédéric Farnir, Nacerredine Kafidi, Ibrahim Njikam Nsangou, Pascal Leroy, Nicolas Antoine‐Moussiaux

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueRevista Brasileira de Zootecnia · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Diversity and Health Studies
Canadian institutionsCanadian Food Inspection Agency
Fundersnot available
KeywordsBreedRanking (information retrieval)ProfessionalizationPurebredPerceptionAgricultural scienceVeterinary medicineMedicinePsychologyBiologyAnimal sciencePolitical science

Abstract

fetched live from OpenAlex

This survey, conducted with 461 racehorse owners-breeders in Algeria between 2009 and 2011, investigates their perceptions, objectives and practices regarding selective breeding. Racehorse breeding is a full-time professional activity for a third of interviewees. The holdings are small-sized with 77% owning one or two mares. The regular practice of mating is here used to categorize breeders according to their degree of professionalization (38.4% professional vs. 61.6% occasional breeders). Experience in the sector was also used to classify breeders, considering as "junior" the breeders under 10 years experience (38.8%) and as "senior" those above 10 years (61.2%). More than professionalization, experience shows a significant impact on practices and objectives. Thus, experience influences breed choice (junior breeders tend to specialize while senior own both Arabian and Thoroughbreds), age at first foaling (sooner among senior breeders), information sources considered for selecting stallions (senior use more diversified sources), the importance granted to the price of mating (greater for junior breeders), the importance granted to the ranking compared to earnings (the ranking being more important to junior breeders), and the priority given to breeding (junior breeders give higher priority to a buy-race-resell activity). Finally, racehorse breeding is poorly professionalized, the only financial goal being cost coverage. Despite inappropriate practices, an interest for selection is noticed.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.084
Threshold uncertainty score0.223

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.032
GPT teacher head0.290
Teacher spread0.258 · 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