Short Communication: Heritability Estimation of Birth Weight of Swamp Buffalo in Sabah, Malaysia
Bibliographic record
Abstract
Data on birth weight of Swamp buffalo calves that were born between 2015 and 2017 were collected and analyzed for this study. The objective was to estimate the effect of heritability and to evaluate the influence of environmental factors on the birth weight of swamp buffalo calves. The heritability was estimated using parent-offspring regression method while the environmental factors were measured using linear regression analysis. The average birth weight for swamp buffalo calves was 31.5 ± 5.33kg. It was significantly (p<0.05) affected by the age of dam and the year of birth but the body weight of the dam and the sex of calves did not significantly (p>0.05) influence the birth weight. The heritability of birth weight was estimated to be 0.29, which is low. Therefore, environmental and herd management factors seem to play a larger role in birth weight than genetics. The low estimated heritability obtained from this work indicates that improvement through selection may not be feasible.
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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 | 0.001 | 0.002 |
| 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) | 0.002 | 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".