Effect of lameness on dairy cows’ visits to automatic milking systems
Bibliographic record
Abstract
Lameness is a major welfare problem for dairy cows and has important economic consequences. On-farm detection of lameness is difficult, and automated methods may be useful for early diagnoses. Lameness may reduce the efficiency of automated milking systems (AMS) if lame cows are less willing to visit the automatic milking unit voluntarily and poor attendance at milking units may help detect lameness. To determine whether a low frequency of visits in an AMS could serve as an indicator of lameness, data on the frequency of visits of 578 cows in 12 AMS on eight farms were collected. From each AMS, 22 cows (from a mean of 54 cows per AMS), were classified as either the 11 highest visitors or the 11 lowest visitors based on the total number of visits to the milking unit. These selected cows (n= 256) were videotaped while walking in a standard test area and their gait scored on a 5-point scale (1 = sound 5 = severely lame). Intra- and inter-observer reliability values between and within observers were high for gait scoring. Significant differences in gait scores between the two groups of cows (P< 0.05) were found in 9 out of 12 AMS: high-visiting cows had better gait scores than low-visiting cows. Four percent of high visitors were classified as slightly lame and 32% of low visitors were classified as either slightly or severely lame. The overall numerical rating score was the most effective in discriminating between high and low visitors, and scoring each individual component of gait did not greatly improve discrimination between the two groups of cows. The frequency that dairy cows visit an AMS is related to their locomotory ability, and data from the AMS may help in the early detection of lameness. Key words: Cattle, lameness, automatic milking systems, behaviour, gait scoring
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".