Cognitive and non-cognitive factors predict pigs’ positions in an aggression social network
Bibliographic record
Abstract
Social network analysis (SNA) provides a means of understanding animals' agonistic behaviour in a group. The aim of this study was to use SNA to characterise how individual cognitive performance affects agonistic behaviour. Using 175 pigs, we hypothesised that their choice of opponents would be affected by their ability to discriminate spatial information and to adapt their behaviour when cues were reversed. A spatial discrimination test was conducted; left and right locations were assigned as positive (food reward) and negative (fan) and each pig's learning speed was recorded. The cues were then reversed, and we tested whether pigs adjusted their behaviour. At age 14 weeks, pigs were regrouped into 14 groups, and their behaviour recorded for 5h, from which weighted and unweighted networks were constructed. Skin lesions were counted after 24h, 1 week and 2 weeks. Males delivered more aggression, and heavier pigs were involved in more aggression. Betweenness centrality (a network position linking otherwise unconnected individuals) increased with network size and decreased with body weight. Passing the reversal learning test predicted more involvement in unilateral aggression. The current study therefore shows links between cognitive performance and aggression and advances the understanding of social network analysis in the context of animal welfare.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".