DETERMINATION OF THE FREQUENCY OF PHENOTYPIC EXPRESSION OF THE COAT IN MANGALARGA MARCHADOR AND QUARTER HORSE BREEDS
Bibliographic record
Abstract
Modern equideoculture constitutes the horse agribusiness complex, whose interest for sport, leisure and work is growing. In this sense, the demand for animals with certain coats aims to meet the associated performance and morphology in the same individual and their descendants. The objective of the study was to determine the FEFP (frequency of phenotypic expression of the coat) and its particularities in horses of the QM (Quarter Mile) and MM (Mangalarga Marchador) breeds. To this end, the study was developed, through remote work, between August 1, 2020 and July 31, 2021, through retrospective research, with free access to the research website and breed associations. In an analysis of 1,029 individuals of the QM breed and 982 of the MM breed, randomly selected, of both sexes, lineages, their respective parents, grandfathers and grandmothers, paternal and maternal, a calculation that aims to record the FEFP and particularities of the coats. The means of the QM breed will be compared using the Scheffé test, all at the 5% significance level. In the MM breed, 19.82% sorrel animals were observed, 5.15% bay, 27.11% chestnut, 1.36% wolf, 19.24% pampa, 8.16% black, 2.62% roan and 11.95% gray. Under the experimental conditions, when comparing male and female animals with different bloodlines: own siblings, paternal siblings, maternal siblings and non-siblings, it is considered that the QM and MM breeds have a casuistry of occurrence of similar coats between sexes and different bloodlines similar to other known horse breeds.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".