Tail-in-mouth behaviour in slaughter pigs, in relation to internal factors such as: age, size, gender, and motivational background
Bibliographic record
Abstract
Tail-in-Mouth (TIM) behaviour has occasionally been observed among pigs living under semi-natural conditions. It is therefore considered to be a normal, low-frequency behaviour. Under certain conditions, TIM behaviour may increase in frequency and progress into tail biting per se. Several factors, such as age, gender, and size, are believed to enhance this development. This study aimed to elucidate the frequency of TIM behaviour among slaughter pigs, specifically in relation to age, gender, size, and group composition regarding gender. Similarly, we intended to characterize the motivational context in which TIM behaviour occurs. Two batches consisting of 24 pigs each, weighing between 40 and 50 kg, were allocated into 3 groups: 1) Eight female pigs; 2) Eight castrated male pigs; 3) Four female and four castrated male pigs (mixed-gender group). Observation was performed by video recording 4 h per day, 1 day per week, for four consecutive weeks. The pigs were weighed once a week during the experiment. The number of TIM events (counts) as well as the identity of the performer and the receiver of TIM behaviour was recorded. The results showed that the frequency of TIM behaviour in the male group was significantly lower than in the female and mixed-gender groups (P=0.003). Size expressed as weight or growth rate did not influence the amount of TIM behaviour performed or received. TIM behaviour was positively related to social exploration and environmental exploration (P<0.001). Finally, TIM behaviour was most often performed while the pigs were standing still or lying down (P<0.001).
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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) | 0.002 | 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".