Automatic detection of feeding- and drinking-related agonistic behavior and dominance in dairy cows
Bibliographic record
Abstract
Accurate assessments of social behavior and dominance relationships in cattle can be time consuming. We investigated whether replacements at the feed bunk and water trough-one type of agonistic interaction-can be used to automatically assess dominance relationships. Our study set out to (1) validate a replacement detection algorithm using combined data from electronic feed and water bins, and (2) investigate the applicability of this algorithm to identify individual dominance scores and group-level social hierarchy in freestall-housed dairy cows. We used 4 groups of lactating cows kept in different group sizes (11 to 20 cows) located at 2 research facilities. In both facilities, feed and water were provided via automated feeding systems. A trained observer recorded all agonistic interactions in the pen over multiple days using video. Data from the electronic feed and water bins for the same days were analyzed using an algorithm to detect replacements (i.e., visits where a receiver cow was competitively replaced by an actor cow). Most agonistic interactions at the feed bunk were replacements. These replacements were associated with a brief interval between the time the receiver cow left the bin and the actor cow took her place; the optimal threshold to detect these replacements varied from 22 to 27 s between groups, independent of stocking density. The recall and precision of an algorithm based upon this threshold was high (on average >0.8), comparable to that of trained human observers. We improved data preparation by controlling for detection errors and included filtering to reduce false positives. This resulted in a >20% decrease in false positives and an increase in precision of 0.043. The dominance hierarchy based upon algorithm-detected replacements was similar to that based upon total agonistic interactions observed in the pen; the Spearman rank correlation coefficient between these hierarchies varied among the groups from 0.81 to 0.96. We conclude that data from electronic feed and water bins can accurately estimate agonistic behavior and dominance relationships among dairy cows.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".