Technical Note: Validation of a System for Monitoring Feeding Behavior of Dairy Cows
Bibliographic record
Abstract
An electronic system has been designed that allows for passive monitoring of feeding behavior of individual cows housed in a free-stall barn. The objective of this study was to validate the data generated by this GrowSafe feed alley monitoring system. Twelve lactating cows were each monitored for 24 h using both the GrowSafe system and time-lapse video. The GrowSafe estimation of number of meals consumed by each cow showed perfect agreement with meal frequency identified using the video recordings. The duration of these meals, as estimated by GrowSafe, was highly correlated with the meal duration derived from the video (R2 = 0.98). Despite the excellent agreement for these meal-based measures, for each cow we found some instances in which the video showed that a cow was present at the feed alley but GrowSafe failed to detect cow presence (12.6% of observations) and a few instances in which the reverse was true (3.5% of observations). However, all the missed or extraneous data from the GrowSafe system were closely associated in time with known periods of feeding. These results indicate that this feed alley monitoring system can provide very good measures of meal frequency and meal duration and reasonable estimates of instantaneous feed alley attendance for loose-housed dairy cattle.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".