Weaning age of calves fed a high milk allowance by automated feeders: Effects on feed, water, and energy intake, behavioral signs of hunger, and weight gains
Bibliographic record
Abstract
Dairy calves are increasingly fed large volumes of milk, which reduces feeding motivation and improves weight gain. However, calves often show signs of hunger and lose weight when weaned off milk due to low starter intake. We examined whether delaying the age at weaning would reduce responses to weaning. Calves were raised in groups of 9 and fed milk, starter, hay, and water with automated feeders. In each group, 3 calves were randomly assigned to 1 of 3 treatments: (1) low-milk, early-weaned: fed 6 L/d of milk and weaned at 47 d of age; (2) high-milk early-weaned: fed 12 L/d of milk and weaned at 47 d; (3) high-milk later-weaned: fed 12 L/d of milk and weaned at 89 d of age. Milk, starter, and hay intakes were recorded daily and digestible energy (DE) intake estimated. Feeder visits were recorded. Before weaning, the high-milk calves drank more milk, ate less starter and hay, but had higher DE intakes, gained more weight, and made fewer visits to the milk feeder than the low-milk, early-weaned calves. During and immediately after weaning, the high-fed, early-weaned calves ate less starter and hay, had lower DE intakes, and gained less weight than the low-milk, early-weaned calves and lost their body weight advantage 7 d after weaning. During and immediately after weaning, the high-milk, later-weaned calves ate more starter and hay and had higher DE intakes, higher weight gains, and made fewer visits to the milk feeder than the high-milk, early-weaned calves. They were still heavier than the low-milk, early-weaned calves 18 d after weaning. Delaying the age at which calves are weaned off milk reduces the drop in energy intake and behavioral signs of hunger that result from weaning.
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 | 0.001 |
| 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 | 0.001 | 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".