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Record W2891596026 · doi:10.1139/gen-2018-0006

Detecting genetic regions associated with height in the native ponies of the British Isles by using high density SNP genotyping

2018· article· en· W2891596026 on OpenAlexvenueno aff
Ilze Skujina, Clare L. Winton, Matthew Hegarty, Robert McMahon, Deborah M. Nash, M. C. G. Davies Morel, Neil McEwan

Bibliographic record

VenueGenome · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and phenotypic traits in livestock
Canadian institutionsnot available
Fundersnot available
KeywordsWithersBiologyPonySingle-nucleotide polymorphismGenotypingSNPGeneticsSNP genotypingGenotypeGeneBody weight

Abstract

fetched live from OpenAlex

Height is an important characteristic in the equine industry although little is known about its genetic control in native British breeds of ponies. This study aimed to map QTL data with the withers height in four pony breeds native to the British Isles, including two different sections within Welsh Cobs. In this study, a genome-wide analysis approach using the Illumina EquineSNP50 Infinium BeadChip was applied to 105 ponies and cobs. Analysis identified 222 highly significant height-associated SNPs (P ≤ 10−5), among which three SNPs on ECA9 have also been previously reported elsewhere. The highest number of significant SNPs associated to height in the native British horses were located on ECA1, ECA8, and ECA16.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.067
Threshold uncertainty score0.133

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.218
Teacher spread0.205 · 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 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

Citations15
Published2018
Admission routes1
Has abstractyes

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