Bibliographic record
Abstract
Proper nutritional management during the first year of the calf’s life is essential in maximizing health and productivity. Feeding a sufficient volume of colostrum early in life is crucial to ensure the transfer of passive immunity and research has begun to characterize the additional bioactive compounds in colostrum and transition milk that can benefit calf development. We know that it is important to feed elevated levels of whole milk or milk replacer during the initial weeks of life, when starter intake is negligible, but further research regarding the effects of feeding large volumes of traditional milk replacers compared to whole milk on calf metabolism, health and development is required. When elevated levels of milk are fed preweaning, calves are often susceptible to production challenges during weaning, which can be mitigated by weaning gradually and later in life. It is also becoming clear that postweaning diets, which are often overlooked, can have profound effects on heifer growth and reproductive development. It is clear that a multitude of differing strategies to raise dairy calves exist; yet, it is up to the dairy research and industry communities to educate producers on specific practices that will maximize heifer development, immunity, health and ultimately the profitability of their operations.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.067 | 0.030 |
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".