Effects of group size on agonistic interactions in dairy cows: a descriptive study
Bibliographic record
Abstract
Group-housed cattle may engage in agonistic interactions over resources such as feed, which can negatively affect aspects of welfare. Little is known about how contextual factors such as group size influence agonistic behaviour. We explored the frequency of agonistic interactions at the feeder when cattle were housed in different-sized groups. We also explored the consistency of the directionality of agonistic interactions in dyads and of the number of agonistic interactions initiated by individuals across the group sizes. Four replicates of 50 cows each were assessed in two group-size phases. In Phase 1, cows were kept in one group of 50. In Phase 2, these same cows were divided into five groups of 10, maintaining stocking density (i.e., ratio of animals to lying stalls and feed bunk spaces). We measured agonistic replacements (i.e., interactions that result in one cow leaving the feed bin and another taking her place) at an electronic feeder using a validated algorithm. We used these data from Phase 1 to calculate individual Elo-ratings (a type of dominance score). Cows were then categorised into five dominance categories based upon these ratings. To ensure a consistent Elo-rating distribution between phases, two cows from each dominance category were randomly assigned to each small group of 10 cows. The mean ± SE number of replacements per cow was similar regardless of whether the cows were housed in groups of 50 (34.1 ± 2.4) or 10 (31.1 ± 4.5), although the groups of 10 were more variable. Further, 81.6 ± 7.7% (mean ± SD) of dyads had the same directionality across group sizes (i.e., the same individual won the majority of interactions in the dyad) and individuals were moderately consistent in the number of replacements they initiated (intraclass correlation coefficient = 0.62 ± 0.11; mean ± SD). These results indicate that the relationship between group size and agonistic behaviour is complex; we discuss these challenges and suggest new avenues for further research.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".