Effects of fat content of high-protein milk replacer on intake and growth performance of Holstein calves in summer and winter
Bibliographic record
Abstract
The objective of this study was to evaluate the effects of fat content of high-protein milk replacer on intake and growth performance of dairy calves in summer and winter. Eighty-four Holstein heifer calves (BW at 7 d of age, 42.2 ± 2.54 kg; mean ± SD) were assigned to 1 of the 3 treatments: milk replacer containing 21% fat (LF; 4.7 Mcal of ME/kg), 26% fat (MF; 5.0 Mcal of ME/kg), and 32% fat (HF; 5.3 Mcal of ME/kg) on a DM basis (n = 14 each for summer and winter). Milk replacers were offered at 600 g/d (powder basis; 3.6 L/d) from 8 to 14 d, increased up to 800 g/d (4.8 L/d) from 15 to 21 d, 1,200 g/d (7.2 L/d) from 22 to 42 d, decreased down to 800 g/d (4.8 L/d) from 43 to 49 d, and 600 g/d (3.6 L/d) from 50 to 56 d, then weaned at 56 d of age. Data and samples were collected until 91 d of age. All the calves were fed a calf starter and chopped hay ad libitum from 7 d of age. In summer, HF group had lower starter intake than LF and MF groups during 51 to 56 d of age, whereas the HF treatment did not decrease starter intake in winter. These results are consistent with a tendency of interaction between treatment and season for body circumference gain during 43 to 56 d; HF group had lower body circumference gain than LF and MF groups in summer but not for winter. In addition, we found a trend for an interaction between treatment and season for withers height gain during 8 to 21 d; withers height gain in winter increased linearly as fat content of milk replacer increased but not for summer. These results suggested that increasing fat content of milk replacer increases growth performance of dairy calves in the early age, and does not decrease starter intake during the weaning transition in winter, whereas it decreases starter intake in summer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.000 |
| 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".