Automated, longitudinal measures of drinking behavior provide insights into the social hierarchy in dairy cows
Bibliographic record
Abstract
Dairy cows compete for feed and water access on commercial farms. In this study we used EloSteepness to assess the summed Elo winning probabilities (i.e., dominance) of 87 cows housed in a dynamic group and compared the resulting social hierarchies based on their steepness (i.e., the average degree of differences in winning probability between adjacently ranked individuals in the group, ranging from 0 to 1). We identified a hierarchy at the drinker with a steepness of 0.55 ± 0.02; whereas the hierarchy detected at the feeder during the same time period was 0.45 ± 0.02, indicating smaller dominance differences among cows when competing for feed compared with competing for water. Individual cows' winning probabilities at the feeder and drinker were moderately correlated (r s = 0.55), and cows at the lower and upper ends of the hierarchy showed good agreement. We compared the drinker hierarchy between hot (i.e., THI ≥ 72) and normal (i.e., THI <72) periods. The hierarchy steepness was similar in both hot (0.54 ± 0.03) and normal conditions (0.56 ± 0.03), and there was a strong correlation in cows' individual winning probabilities across these periods (r s = 0.87). Cows with higher winning probability visited the drinker less frequently (hot: r s = −0.40, normal: r s = −0.33) but had a higher average daily water intake (hot: r s = 0.38, normal: r s = 0.37). We also found evidence that individual cow's drinking times differ depending on their winning probability; cows with lower winning probability shifted their drinking times to before or after the visit peak after milking. Automatically identifying cows with consistently high or low winning probabilities using drinkers may help inform grouping decisions and water provision on farms.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".