Soymilk as a Novel Milk Replacer to Stimulate Early Calf Starter Intake and Reduce Weaning Age and Costs
Bibliographic record
Abstract
The primary objective was to determine the effects of partial replacement of whole milk with soymilk on preweaning calf performance and weaning costs. Following 3 d of colostrum and transition milk feeding, 18 male and 9 female neonatal Holstein calves (41.6 +/- 1.6 kg of body weight; mean +/- SE) were assigned in a completely randomized design to 3 treatments offered at 10% of body weight (wet basis) including 1) whole milk (control), 2) 75% whole milk + 25% soymilk (S25), or 3) 50% whole milk + 50% soymilk (S50). The weaning criterion was defined as the calf age at a daily intake of >/=900 g of starter concentrate. During the first 2 wk of the experiment, treatments did not differ in starter intake and fecal score. Calves fed S25 gained similar amount of body weight as calves fed whole milk diet. By 49 d of age, also, calves on S25 gained similar body weight as did calves on whole milk diet. Calves on S25 and S50 achieved the weaning criterion, respectively, about 10 and 12 d earlier than did peers on whole milk. The soymilk-fed calves consumed less milk than control calves to meet the weaning criterion owing to promoted starter intake. Feed-related weaning costs dropped by about 35% when soymilk was fed because whole milk was about 50% more expensive than both soymilk and starter concentrate. Feeding soymilk at up to 50% of the milk diet maintained health during the first 2 to 4 wk of age when the neonate calf is highly sensitive to nonmilk proteins and plant antinutrients. Results introduce soymilk as an economic partial substitute for whole milk in calf-raising facilities.
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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".