Short communication: Automatic detection of social competition using an electronic feeding system
Bibliographic record
Abstract
The objective of this study was to determine if data derived from a system that electronically monitors feeding behavior could be used to identify competitive interactions of dairy cows at the feed bunk. A short interval between successive feeding events of 2 cows at 1 feed bin was predicted to be associated with a competitive replacement: when one cow displaced a feeding cow and then took her position at the bin. To identify the interval between feeding events that best predicted these replacement events, the feeding activity of 5 Holstein dairy cows was monitored using an electronic feeding system and video recordings. The number of times a cow was replaced at the feed bunk over 3 consecutive 24-h periods was determined using video analysis and these events were paired with the corresponding feeding events recorded by an electronic feeding system (Roughage Intake Control system; Insentec B.V., Marknesse, the Netherlands). A pooled analysis of all 5 cows showed that the optimal interval for predicting replacements at the feed bunk was 26s (sensitivity=86% and specificity=82%); this interval was termed the replacement criterion. This criterion was then applied to feeding data from a sample of 24 independent Holstein dairy cows, each observed for 3d during the week following calving. Video had previously been used to measure the number of times each cow was an actor and reactor of a displacement (when one cow displaced a feeding cow but did not necessarily take her position at the bin). Despite the differences in measures, the number of replacements (as estimated by our algorithm) was positively correlated with the number of displacements [as measured using video; correlation coefficient (r)=0.63 as actor, r=0.69 as reactor]. Estimates of an index of success in competitive interactions (number of times actor/number of times actor = number of times reactor) generated using the 2 methods were highly correlated (r=0.94). These results suggest that competitive behavior at the feed bunk can be automatically quantified using data derived from an electronic feeding system.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".