MétaCan
Menu
Back to cohort
Record W2126544823 · doi:10.1093/jhered/esu074

The Legend of the Canadian Horse: Genetic Diversity and Breed Origin

2014· article· en· W2126544823 on OpenAlexaboutno aff
Anas M. Khanshour, Rytis Juras, Rick Blackburn, E. Gus Cothran

Bibliographic record

VenueJournal of Heredity · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and phenotypic traits in livestock
Canadian institutionsnot available
Fundersnot available
KeywordsBreedGenetic diversityInbreedingPonyBiologyMicrosatelliteDiversity (politics)ZoologyGenealogyGeographyVeterinary medicineDemographyEcologyHistoryPopulationGeneticsAnthropologyAlleleSociology

Abstract

fetched live from OpenAlex

The Canadian breed of horse invokes a fascinating chapter of North American history and as such it is now a heritage breed and the national horse of Canada. The aims of this study were to determine the level of genetic diversity in the Canadian, investigate the possible foundation breeds and the role it had in the development of the US horse breeds, such as Morgan Horse. We tested a total of 981 horses by using 15 microsatellite markers. We found that Canadian horses have high values of genetic diversity indices and show no evidence of a serious loss of genetic diversity and the inbreeding coefficient was not significantly different from zero. Belgian, Percheron, Breton and Dales Pony, unlike the light French horses, may have common ancestries with the Canadian and could be important founders. However, the Shire and Clydesdale influenced the Canadian to a lesser extent than French and Belgian draft breeds. Furthermore, our finding indicated that there was no evidence of a clear relationship between Canadian and Oriental or Iberian breeds. Also, the Canadian likely contributed to the early development of the Morgan. Finally, these findings support the ancient legends of the Canadian Horse as North America’s first equine breed and the foundation bloodstock to many American breeds and may help in the management and breeding program of this outstanding breed in North America.

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)0.000
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 score0.045
Threshold uncertainty score0.993

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 head0.011
GPT teacher head0.210
Teacher spread0.199 · 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

Citations12
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueJournal of HereditySame topicGenetic and phenotypic traits in livestockFrench-language works237,207