Duration of weaning, starter intake, and weight gain of dairy calves fed large amounts of milk
Bibliographic record
Abstract
When calves are weaned abruptly off large amounts of milk, weight gain is reduced as a result of low intake of starter. We compared gradual and abrupt weaning of 40 calves allowed to drink up to 12kg of milk/d by automated feeders, housed in groups of 4, and weaned at 41 d abruptly or over 3 gradual weaning periods (4, 10, or 22 d), with one calf within each group randomly allocated to each treatment, balancing for sex and birth weight. During the milk-feeding period, the calves weaned over 22 d drank the least milk and ate the most starter, but these calves had the lowest total digestible energy intake and weight gains. The abruptly weaned calves had the highest digestible energy intakes and weight gains during the period before weaning. During the 9 d following weaning, the calves weaned over 22 and 10 d ate more starter and had better weight gains than abruptly weaned calves and those weaned over 4 d. Abruptly weaned calves lost weight during this period. In summary, gradual weaning improved starter intake, but because of reduced milk availability, this resulted in reduced total digestible energy intake before weaning. Weaning over 10 d resulted in the best overall weight gains over the study.
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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 | 0.001 |
| 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".