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Record W2137775024 · doi:10.1017/s1751731111001212

Genetic diversity and admixture among Canadian, Mountain and Moorland and Nordic pony populations

2011· article· en· W2137775024 on OpenAlexaffabout
Jaclyn Mercedes Prystupa, Rytis Juras, E. Gus Cothran, Fiona Buchanan, Y. Plante

Bibliographic record

Venueanimal · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic diversity and population structure
Canadian institutionsUniversity of SaskatchewanAgriculture and Agri-Food Canada
Fundersnot available
KeywordsPonyGenetic diversityBreedBiologyMoorlandPopulationZoologyEcologyGeographyDemographyGenetics

Abstract

fetched live from OpenAlex

As part of the requirements of the Convention on Biological Diversity, Canada has been investigating the genetic diversity of its native equine and pony populations. Along with examining four indigenous Canadian equine populations (Canadian horse, Lac La Croix pony, Newfoundland pony and Sable Island population), another 10 Mountain and Moorland, three Nordic, four horse and two feral equine populations (thought to have influenced some pony breeds) were also investigated. In total, 821 individuals were genotyped at 38 microsatellite loci. Results of the analysis of molecular variance indicated that 13.3% of genetic diversity was explained by breed differences, whereas 84.6% and 2.1% of diversity came from within and among individuals, respectively. The average effective number of alleles and allelic richness was the lowest in the Eriskay (2.51 and 3.98) and Lac La Croix (2.83 and 4.01) populations, whereas it was highest in the New Forest (4.31 and 6.01) and Welsh (4.33 and 5.87) breeds, followed closely by the Newfoundland-CDN (4.23 and 5.86) population. Expected heterozygosities varied from 0.61 in the Lac La Croix to 0.74 in the Welsh and in Newfoundland. Observed heterozygosities ranged from 0.57 in the Exmoor and 0.58 in the Sable Island herd to 0.77 in the Kerry Bog and 0.76 in the New Forest breeds. Structure and admixture analyses revealed that the most likely number of clusters was 21, although some substructure was also observed when K = 16, compared with the 24 predefined populations. Information gathered from this study should be combined with other available phenotypic and pedigree data to develop, or amend, a suitable conservation strategy for all populations examined.

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 score0.031
Threshold uncertainty score0.981

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 head0.022
GPT teacher head0.208
Teacher spread0.185 · 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

Citations29
Published2011
Admission routes2
Has abstractyes

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