Selection of Black Bengal Buck Based on Some Reproductive Performance of Their Progeny at Semi-Intensive Rearing System
Bibliographic record
Abstract
The objective of this study was to select Black Bengal Buck Based on some reproductive performance of their progeny. The least-squares means for overall reproductive performances of age of first kidding (AFK), weight at first kidding (WFK), gestation length (GL), kidding interval (KI), post-partum heat (PPT), litter size at birth (LS) and litter weight at birth were 465.6 days, 13.22 kg, 145.34 days, 302.5 days, 123.84 days, 1.61 and 1.66 kg, respectively. The effect of flock and generation were significant (p<0.05) for AFK, WFK, KI, PPT, LS and LWB. The effect of parity of doe was significant (p<0.01) for LSB and LWB. The effect of season was significant (p<0.05) for KI, PPT and LWB. The heritability value for these traits was ranges from 0.17 to 0.24 and predicted breeding value from -0.003 to 0.445. According to the genetic worth of the buck the highest breeding value of reproductive traits were found in the progeny of Buck No. 32, followed by Buck No. 52, 54, 81 and 87. This progeny tested bucks may be used for the improvement of the reproductive potentials of Black Bengal goat through Artificial Insemination (A.I). The lowest breeding value of reproductive traits was found from the progeny of Buck No. 3 followed by Buck No. 11. The low estimates of heritability obtained for reproductive traits indicated that selection based on the doe’s own performance may result in slow genetic improvement therefore; the progeny testing program will be beneficial to the farmers and fulfill their need by supplying superior sires of high genetic merit.
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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 | 0.000 | 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".