The effect of access to a well-resourced environment on dairy calves' cognition and affective states
Bibliographic record
Abstract
This study aimed to explore the effect of environmental complexity on the ability of dairy calves to discriminate between conspecifics and on their sensitivity to reward. Calves were housed either in 1) pair housing for 22.5 h/d with 1.5 h of daily access to a well-resourced pen which included 3 other calves and physical devices (enriched calves, n=6 pairs) or 2) pair housing for 24 h/d (control calves, n=6 pairs). After 10 d of housing treatment, calves were trained to discriminate between 2 calves in a Y-maze over 20 d. Twelve of the 24 calves tested met the learning criterion and treatment did not affect the number of sessions needed to reach the learning criterion. Calves were then subjected to a Successive Negative Contrast test during which they were trained to approach a milk reward over 3 trials/day for 3 days. On the last training day, latencies of enriched calves increased over daily trials while control calves were faster and remained relatively consistent, suggesting a greater sensitivity to reward. Starting on day 4, the reward was reduced for the 5 following test days. On test days, calves’ latencies to reach the reward increased across daily trials but no effect of treatment or days was found. Our findings suggest that calves can discriminate among individuals but learning performance did not differ between treatments. Calves raised in standard pair housing showed increased sensitivity to reward, supporting they may experience a more negative emotional state in comparison to calves reared with temporary access to a well-resourced environment
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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 | 0.001 | 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".