Carcass Weight, Meat Yield and Meat Cuts From Arado, Boran, Barka, Raya Cattle Breeds in Ethiopia
Bibliographic record
Abstract
This study was conducted with the objective to evaluate carcass weight, meat yield and primal meat cuts of beef from Arado, Boran, Barka, Raya and nondescript cattle breeds slaughtered at export abattoirs in Ethiopia. Data was collected from Abergelle and Melgawendo export abattoirs in 2011. The result of the study revealed that the average live weight, warm carcass weight, cold carcass weight and warm dressing percentage of cattle slaughtered at the abattoirs studied were 241.41±0.37 kg, 106.93±0.21 kg, 101.19±0.18 kg and 44.21±0.05%, respectively. Live weight, carcass weight and dressing percentage were differ (P < 0.001) between abattoirs, seasons and breeds of cattle slaughtered. Average meat yield and yield percentage of cattle slaughtered at Abergelle abattoir was 61.56±0.94 kg and 67.81±0.33%, respectively. Meat yield and weight of primal meat cuts were different (P < 0.001) between breeds of cattle. Yield percentage was significantly (p < 0.05) different between seasons. Retailed meat yield was significantly predicted (R2 = 88.1%) from slaughter weights, Topside (R2 = 77.86), Silverside (75.64), Knuckle (R2 = 70.13), Striploin (R2 = 70.73), Tenderloin (R2 = 61.33), Shank (R2 = 64.55) and Rumpcap (R2 = 64.48). From the study it was concluded that Boran cattle was better in dressing percentage compared to most cattle breeds in Africa while the dressing percentage and meat yield of Arado, Barka and Raya breeds were less than the percent and yield reported for other zebu cattle in Africa. A strategy should be devised to improve the carcass weight, dressing percentage and retail able meat yield from local cattle in Ethiopia.
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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 | 0.000 |
| 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 | 0.001 | 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".