Bibliographic record
Abstract
Conventional milk feeding for calves (by bucket twice daily at a total of 10% of body weight) was compared with feeding milk for ad libitum consumption from a nipple. Calves were weaned gradually between d 37 and 42 by diluting the milk with water, and body weight and feed consumption were followed until d 63. Calves fed ad libitum drank 89% more milk than calves fed conventionally during the preweaning period, but the ad libitum-fed calves ate only 16% as much calf starter and 17% as much hay. Consumption of starter and hay increased rapidly after weaning, and treatment differences disappeared. Probably as a result of the much higher intake of milk, the ad libitum-fed calves gained 63% more weight than the conventionally fed calves before weaning, resulting in a 10.5-kg weight advantage on d 35. During and immediately after weaning, the rate of weight gain slowed for both treatment groups, but recovered by approximaely d 49. There were no treatment differences in weight gains over the weaning or postweaning periods, and at the end of the experiment on d 63, the calves fed ad libitum maintained an advantage in mean (+/- SEM) body weight (89.07 +/- 2.47 kg vs 81.07 +/- 2.47 kg for the conventionally fed calves). Incidence of diarrhea was low and did not differ between treatment groups. We conclude that ad libitum nipple feeding of milk to dairy calves can allow for increased milk intake and weight gain with no detrimental effects on intake of solid food after weaning.
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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) | 0.001 | 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) | 0.001 | 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".