Survival analysis and reproductive performance of Dorper x Tumele sheep
Bibliographic record
Abstract
Productivity and profitability of sheep farming are highly influenced by lamb survival and ewe reproductive performance. Thus, this study was conducted to evaluate the survival and reproductive performance of crossbred sheep. Data collected from 2009 to 2018 from Sirinka sheep breeding stations were utilized for this study. Survival analysis was conducted by using Survival Kit 6.12 software with the Weibull model and the general linear model of SAS 9.0 was used to analyze reproductive traits. The overall mean survival rate of Dorper x Tumele crossbred lambs at 3, 6 and 12 months of age were 86.0, 76.6, and 67.9%, respectively. About 46.8% of mortality from the total death was observed during the first 120 days of life. Gastrointestinal parasites, pneumonia and septicemia were the major causes of lamb mortality. Birth weight, birth type, sex and year of lambing were the most important risk factors for survival of crossbred lambs. The overall least-squares means for litter size at birth, litter size at weaning, total litter weight at birth and total litter weight at weaning were 1.10 lambs, 0.94 lambs, 3.28 kg and 15.5 kg, respectively. Birth type, sex and year of lambing were the most determinants of ewe productive traits. Tumele and their crossbred sheep had good mothering ability necessary to successfully raise lambs to weaning. The current crossbreeding program which aims to improving growth performance had a positive influence on the survival rate of lambs. Improvement of environmental in the flock, special care for small lambs and indirect selection based on birth weight would lead to further survival improvement.
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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) | 0.000 | 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 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".