Separation from the Dam Causes Negative Judgement Bias in Dairy Calves
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it