Morphological diversity of northeastern fat-tailed and northwestern thin-tailed indigenous sheep breeds of Ethiopia
Bibliographic record
Abstract
Characterization of indigenous sheep breeds using morphological traits is essential for designing rational conservation and improvement strategies. This study was conducted to check the morphological diversity of three fat-tailed and three thin-tailed indigenous sheep breeds of Ethiopia. The phenotypic traits such as live body weight and linear body measurements (body length, wither height, chest girth, chest depth, rump height, rump length, ear length, tail length, and pelvic width) were measured and used for analysis. The statistical analysis was done using different procedures of SAS 9.4. Analysis of variance showed significant variation between breeds. Multivariate analyses clearly assigned the studied sheep breeds into distinct populations. Mahalanobis distance showed significant (p < 0.01) difference between breeds. The present morphometric information obtained could support future decision-making on the management, conservation, and improvement of the studied sheep genetic resources.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".