Replication Data for: Automated, longitudinal measures of drinking behaviour provide insights into the social hierarchy in dairy cows
Bibliographic record
Abstract
Water is an essential resource for dairy cattle, and in some circumstances cattle will compete with one another to gain access to water. Here we applied a Bayesian-based, Elo-rating method to assess the winning probabilities of 87 cows housed in a dynamic group and compared the resulting social hierarchies based on their steepness. 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 less steep (0.45±0.02), indicating smaller average differences between the winning probabilities of cows when competing for feed compared to competing for water. Individual cows’ winning probabilities at the feeder and drinker were moderately correlated (rs=0.55, P<0.001). However, cows at both the lower and upper ends of the hierarchy demonstrated a consistent alignment. We compared drinker hierarchies between periods with THI above and below 72 and found similar steepness (0.54±0.03 and 0.56±0.03 respectively) and the individual winning probabilities of cows were highly correlated between hot and normal periods (rs=0.87, P<0.001). Individual drinking behaviour was also associated with the drinker hierarchy, cows with higher winning probability had lower average daily visit frequency (hot: rs=-0.40, P<0.01, normal: rs=-0.33, P<0.01) and higher average daily water intake (hot: rs=0.38, P<0.01, normal: rs=0.37, P<0.01). We also found evidence that cows differ in when they drink, depending on their winning probability; less successful cows 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 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 | 0.018 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.011 |
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".