PSXVII-32 Late-Breaking: Weaning of ad libitum fed dairy calves with automated feeders using fixed and individual methods.
Bibliographic record
Abstract
The increasing availability of automated milk dispensers on dairy farms facilitates ad libitum milk supply but weaning calves from high milk allowances is a challenging process. This study evaluated different gradual weaning methods for their impact on starter intake and growth. Thirty-six Holstein (n = 30) or crossbred (n = 6) bull calves were individually housed from d 2–14 of age and had ad libitum access to milk replacer from teat buckets. From d 15–84 of age, calves were grouped and had ad libitum access to milk replacer, starter, straw and water from automated feeders. At d 35, calves were blocked (age and breed), and randomly assigned to a weaning method: (1) Linear (LIN), milk supply was stepped down to 6 L/d on d 36, and linearly reduced between d 36 and 63 from 6 to 2 L/d. (2) Step-down (STEP), milk supply was stepped down to 6 L/d from d 36–48, 4 L/d from d 49–56 and 2 L/d from d 57–63. (3) Dynamic (DYN), at d 36, milk supply was reduced for each individual calf to 75% of the average voluntary consumption between d 29–35, then maintained for 9 d, and then reduced to 50% for 10 d, and to 25% for 9 d. DYN calves received more milk during weaning than LIN calves, whereas STEP calves had intermediate milk intake. Starter intake was not affected by weaning method. DYN calves grew faster and were heavier than STEP calves during post-weaning period, whereas LIN calves performed similarly to DYN calves (1.329 ± 0.082, 1.099 ± 0.082 and 1.228 ± 0.082 kg/d; 123.5 ± 2.41, 116.6 ± 2.41 and 120.4 ± 2.41 kg at d 84). These results highlight the post-weaning benefits of DYN and LIN weaning methods when compared with more abrupt step-down strategies.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".