Phenotypic Correlation Between Body Measurements in Saudi Sheep in Qassim Region
Bibliographic record
Abstract
Identify the genetic resources of the sheep and characterize these breeds accurately are very important to enhance the good performances of sheep and expand the knowledge of the differences among those breeds. Body measurements therefore, are perfect indicators to make definition for each breed. The present study is part of a wide one to definite of phenotypic characteristics in local breeds of sheep in Qassim region, Kingdom of Saudi Arabia. The data were collected from three breeds in Qassim region, Noemi, Najdi and Hari. Najdi is the biggest breed of the sheep breeds in Saudi Arabia and it is the main breed in Najd region. Noemi is taking the second size breed of the sheep breeds in Saudi Arabia. Hari is the smallest breed of sheep breeds in Saudi Arabia, it is the main breed in Hejaz and Assir regions, which belong to the sheep with coarse hair, and thick tail strain. Eight body measurements traits were studied, Wither heights (WH), Rum heights (RH), Body length (BL), Head length (HL), Heart girth (HG), Muzzle diameter (MD), Cannon circumference (CC) and Cannon length (CL). The correlations coefficients among all studied traits were moderate to high and highly significant. The highest correlation coefficient was found between RH and WH traits (0.872), and the lowest one was found between CC and HG traits (0.214). The correlations coefficients between relative traits can help us to understand the similarity among studied traits and can be used in the future in selection program.
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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 | 0.000 | 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".