Separation from the Dam Causes Negative Judgement Bias in Dairy Calves
Bibliographic record
Abstract
Negative emotional states in humans are associated with a negative (pessimistic) response bias towards ambiguous cues in judgement tasks. Every mammalian young is eventually weaned; this period of increasing nutritional and social independence from the dam is associated with a pronounced behavioural response, especially when weaning is abrupt as commonly occurs in farm animals. The aim of the current study was to test the effect of separation from the cow on the responses of dairy calves in a judgement task. Thirteen Holstein calves were reared with their dams and trained to discriminate between red and white colours displayed on a computer monitor. These colours predicted reward or punishment outcomes using a go/no-go task. A reward was provided when calves approached the white screen and calves were punished with a timeout when they approached the red screen. Calves were then tested with non-reinforced ambiguous probes (screen colours intermediate to the two training colours). "GO" responses to these probes averaged (± SE) 72±3.6% before separation but declined to 62±3.6% after separation from the dam. This bias was similar to that shown by calves experiencing pain in the hours after hot-iron dehorning. These results provide the first evidence of a pessimistic judgement bias in animals following maternal separation and are indicative of low mood.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 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".