Milking time behavior of dairy cows in a free-flow automated milking system
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The objective of this preliminary observational study was to determine milking time behavior of cows in a free-flow automated (robotic) milking system (AMS) and identify potential factors that influenced the time waiting to be milked. Milking time behavior of 40 cows from 1 pen on a commercial dairy farm with a free-flow AMS was evaluated using video analysis over 2 d. For each study cow, data were assessed for waiting time to access the milking robot, the use of the fetch pen, robot refusals, and their lying behavior. On average, cows visited the robot to wait to be milked 6 times per day, for 15 min per visit, for a total daily waiting time of 88 min per cow (range 5 to 322 min). Daily waiting time was longer for primiparous cows and decreased with increasing days in milk, but this effect interacted with parity. Daily waiting time and number of visits to the robot were associated with voluntary use of the fetch pen. Furthermore, cows with long daily waiting times had shorter daily lying times compared with cows with short daily waiting times (9.5 vs. 11.1 h/d). It is possible that factors related to the design and layout of the AMS entry and fetch pen had an effect on waiting behavior. We inferred that adoption of grouping strategies intended to reduce competitive behavior, especially toward primiparous cows, could improve milking time behavior in a free-flow AMS. This preliminary observational data from a single herd highlights the need to confirm the findings across multiple AMS herds, both with free-flow and guided-flow systems.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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 it