Measurement of cow comfort during milking on different cluster removal settings through the use of leg-mounted accelerometers
Bibliographic record
Abstract
Increasing levels of data are routinely collected on modern dairy farms. These include multiple variables measured by milking machine sensors and software and cow-attached sensor data, used predominantly for fertility and health monitoring. Following milking efficiency principles, including milking gently, quickly, and completely, there is utility in investigating how various milking machine settings affect gentleness of milking through a proxy measurement of cow comfort during milking. The use of leg-mounted accelerometers was investigated as a noninvasive labor-efficient means of estimating cow comfort on different automatic cluster remover (ACR) milk flow-rate switch-point settings. Accelerometer step count measurements during milking were collected from 37 cows divided into 2 groups allocated to either an ACR milk flow-rate switch-point setting of 0.2 kg/min or 0.8 kg/min for a 2-wk period and then crossed over to the other setting. Significantly more rear leg stepping occurred during daily milking (combined step count during a.m. and p.m. milkings) where the ACR activated at 0.2 kg/min (11.7 steps) compared with 0.8 kg/min (10.1 steps). Shorter milking interval between a.m. and p.m. milkings resulted in lower udder fill and reduced milk flow-rate. Under these lower udder fill conditions, rear leg movement, as an indicator of cow comfort, reduced when milk flow-rate switch-point for cluster removal increased from 0.2 kg/min (5.75 steps) to 0.8 kg/min (4.96 steps). There was no significant difference between stepping rates on both cluster removal settings during a.m. milkings. Similarly, no significant differences were noted in assessed postmilking teat condition, which was conducted after a.m. milking. The 0.2 kg/min setting extended total daily milking time by 70 s, resulting in lower mean flow-rates while producing similar milk yield. Higher vacuum levels at the teat-end were also recorded on this milking setting. This provides further incentive to consider cluster removal settings above 0.2 kg/min.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".