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Record W2990553252 · doi:10.3138/mous.16.3.006

Breeding Success: The Creation of the Racehorse in Antiquity

2019· article· en· W2990553252 on OpenAlexvenueno aff
Carolyn Willekes

Bibliographic record

VenueMouseion Journal of the Classical Association of Canada · 2019
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Equine Medical Research
Canadian institutionsnot available
Fundersnot available
KeywordsVictoryDomesticationCompetition (biology)Variety (cybernetics)AthletesAnimal husbandryHistoryPolitical scienceLawArchaeologyBiologyComputer scienceEcologyAgricultureMedicine

Abstract

fetched live from OpenAlex

The social significance of equestrian competition in antiquity is well documented. Likewise, we know the chronological history of the various events, and a variety of sources—such as the epigraphic record, Pindar, Posidippus, and victory lists—give us a reasonably good idea of who won, when, and where. When it comes to logistics, however, we know very little; this becomes even more apparent when we compare horse sports with other athletic events. In terms of the practicalities of breeding equine athletes in the ancient world, we have relatively little specialized primary material to work with. This article seeks to fill the gaps in our knowledge by taking a comparative approach to the topic. From a physiological standpoint, the horse has changed little since its domestication. The basic nutritional requirements, husbandry methods, and training approaches have remained quite static. This is particularly true in the breeding and training of racehorses, where tradition runs deep, even in the world of Thoroughbred racing. By comparing several aspects of modern racehorse breeding with the evidence from antiquity, we can begin to build a more comprehensive picture of the logistics of equestrian competition, adding not only to our understanding of equines in the ancient world, but also to the field of ancient athletics.

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)0.003
metaresearch head score (Gemma)0.004
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 score0.356
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
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 science0.0010.000
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 head0.027
GPT teacher head0.307
Teacher spread0.280 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations3
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueMouseion Journal of the Classical Association of CanadaSame topicVeterinary Equine Medical ResearchFrench-language works237,207