Running wheel activity in mink with different forms of abnormal behaviour
Bibliographic record
Abstract
Stereotypies can take many different forms but many studies and the WelFur protocol pool these together. However, it is currently unknown if these different forms of stereotypic behaviour share a similar motivational background and have equal welfare significance for the mink. As part of a larger study addressing this issue, we investigated whether free access to a running wheel would substitute, i.e. reduce the prevalence of, the various forms of stereotypic behaviour (SB). We screened in 2019 c. 1100 Palomino and Brown mink dams, individually housed at the AU-farm, into six groups based on their behavioural phenotype: CONTROL (n=18) free from abnormal behaviour, FURCHEW (n=14) fur-chewing, ORALSB (N=11) with licking SB, STATSB (n=15) with stationary SB, PACERS (n=16) with pacing, and MIXED (n=14) with several forms of abnormal behaviour. These 88 mink were relocated to cages with running wheel access for 10 days. We analysed running wheel activity as rounds per days (rpd, i.e. per 24h) using repeated measures mixed ANOVA. The runningwheel naïve mink dams started more or less immediately to use the running wheels (avg. per mink 960 rpd on the first day, 1025 rpd on day 10). There was a considerable variation in running wheel activity between mink. The major finding was that running wheel activity differed between groups with PACERS, STATSB and MIXED groups using the running wheel significantly more (1474, 1404 and 1753 rpd, respectively) than the other groups (P<0.001; avg. 336-467 rpd). Thus, different forms of abnormal behaviour influence the running wheel activity in mink. Results on the effects on stereotypic behaviour will be presented.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".