Social housing improves dairy calves' performance in a competition test
Bibliographic record
Abstract
On most dairy farms, calves are housed individually until weaning. However, depriving calves of an early social environment impairs behavioral development. We studied the effect of early-life social housing on calves' competitive skills. In this study, Holstein heifers were pseudorandomly assigned to either individual housing (n = 9) or pair housing (with a nonfocal companion, n = 9) at the age of 11 d. After 14 d of housing treatment, calves underwent a competition test for milk access against a group-reared calf; consisting of 2 test sessions per day for 5 d (session duration: 74.42 ± 2.29 s; mean ± standard error). Pair-housed calves performed better than individually housed calves: throughout the competition days, individually housed calves increased their latency to approach the milk bottle and decreased their time spent drinking in contrast to pair-housed calves, which exhibited stable latencies to reach the milk bottle and increased their time drinking. To control for the influence of personality on their competitive abilities, all calves were subjected to personality tests assessing boldness before being exposed to the housing treatment. Our findings indicate that calves assessed as bolder during the pretreatment personality test tended to approach the milk bottle faster. Our results provide additional evidence of the beneficial effects of social housing on dairy calves' behavioral development.
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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 | 0.001 |
| 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".