Trade-offs between proximity and physical contact during group integration in pigs ( <i>Sus scrofa domesticus</i> )
Bibliographic record
Abstract
Abstract During behavioral trade-offs, individuals have to decide whether to express a behavior which may lead to a reward or potential costs when engaging in a risky situation. Social integration forces animals to make such trade-offs. We hypothesized that animals predominantly demonstrate nontactile behavior and hence less tactile behavior in a high-risk context such as during social integration, while using tactile behavior more than nontactile in less risky situations such as under social stability. Pigs (Sus scrofa domesticus) typically are in close physical contact to each other, but physical contact also relates to increased aggression. We investigated 18 groups (142 pigs) across different phases of social stability, thereby observing snout proximity and snout contact. Additionally, aggression (reflecting costs) and growth performance (reflecting benefits) were measured. Data were analyzed using mixed models while accounting for group stability. Snout proximity was indeed most frequent during social instability and reduced as stability increased, while snout contact remained more constant. The high occurrence of snout proximity during social instability suggests conflict avoidance and thus risk aversion. Animals that showed more frequent snout proximity grew slower, while initiators and recipients of frequent snout contact had a better growth performance. The causality of these effects cannot be ascertained, but it is possible that slower growing, and thus weaker individuals may have made a behavioral trade-off by choosing proximity rather than contact during social instability. The results further emphasize the importance of distinguishing between nuances in behavior.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".