Biometric profile of Quarter Horses in the region of Manaus, Brazil
Bibliographic record
Abstract
Quarter Horse breed (QH) has been more recently used in sports in Northern Brazil, however it does not have yet biometric evaluation in order to compare to horses from other Brazilian regions, where QH has a larger historic. Therefore, the aim of our study was to assess and present the biometric profile of Quarter horse breed raised and used in sport activities (barrel racing and vaquejada) in the region of Manaus, AM, Brazil. For this purpose, eighty-two (82) QH, adults, were evaluated through photographs analyzed by the ImageJ® 1.46r software. Eight (8) linear morphometric measurements were performed per animal, namely: Withers height (WHe); Croup height (CrH); Codilho height (CoH); Body length (BL; Neck length (NL); Dorsal-lumbar length (DLL); Scapula length (SL) and Head length (HL). Our results were within the racial standard demanded by the Brazilian Quarter Horse Breeders Association, which demonstrates a racial standardization in the region. The animals were classified as having medium size, eumetric. Regarding the average values (in cm), we obtained: WHe of 147.53 (142.76 to 155.33), CrH of 147.38 (141.12 to 154.48), CoH of 83.13 (81.51 of 87.07), BL of 149.15 (147.20 to 152.70), NL of 57.12 (55.2 to 57.3), DLL of 54.94 (52.9 to 57.0) SL of 54.35 (53.4 to 55.20) and HL of 63.70 (62.20 to 64.60). Our findings suggest similarity between the animals of the Quarter Horse breed raised Manaus-AM region with animals from other Brazilian regions. as well as standardization within the required racial parameters. All animals showed good proportions for the barrel racing and vaquejada practices.
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 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 | 0.001 |
| 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".