PSXIII-B-15 Late Weaning Improves Growth and Reduces the Breeding age in Alpine Dairy Goats
Bibliographic record
Abstract
Abstract The aim was to evaluate the effect of weaning age on growth in Alpine dairy goats. Thirty-six female kids were randomly assigned to one of 3 treatments: 1) early weaning at 6 wk of age (6-wk), 2) weaning at 8 wk of age (8-wk), and 3) late weaning at 10 wk of age (10-wk). Treatments were blocked by BW and birth date, and each female kid was housed with a male companion of similar age and BW. Kids had ad libitum access to acidified milk replacer (ME=4.7 Mcal/kg DM), kid starter (ME=2.35 Mcal/kg DM), hay (ME=1.26 Mcal/kg DM) and water. Weaning was performed progressively over 7 d. For the 12 wk period, feed intakes for 6-wk, 8-wk and 10-wk were respectively: 135, 201 and 287 g/d for milk replacer; 90, 68, 54 g/d for hay; 25, 19 and 13 g/d for starter feed. Total ME intake was higher when increasing weaning age (6-wk, 281 kcal/d; 8-wk, 303 kcal/d; 10-wk, 371 kcal/d, P< 0.05). Interestingly, at weaning, total ME intake dropped more markedly when weaning was early (6-wk, 95 kcal/d; 8-wk, 297 kcal/d; 10-wk, 451 kcal/d, P< 0.05). 10-wk had higher BW at 12 wk of age (23.9 kg), compared with 6-wk (21.2 kg, P=0.004), and a trend was observed with 8-wk (22.2 kg, P=0.059). This effect of weaning age on BW was prolonged over time with heavier BW in 10-wk at 8 months (36.1 kg), compared to 6-wk (33.6 kg, P=0.04), and a trend was observed with 8-wk (34.0 kg). Using growth curves, extending the weaning age of dairy goats allowed to reach the breeding BW target of 32 kg sooner (6-wk, 191 d; 8-wk, 188 d; 10-wk, 161 d, P< 0.05). Overall, weaning kids at 10 wk limited the negative impact of earlier weaning on growth in the Alpine breed.
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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 | 0.001 |
| 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".