Technical note: Assessment of milk temperature measured by automatic milking systems as an indicator of body temperature and fever in dairy cows
Bibliographic record
Abstract
The objective of this study was to evaluate whether milk temperature (MT) measured by automatic milking system (AMS) is a reliable indicator of body temperature of dairy cows and whether cows with fever could be detected. Data loggers (Minilog 8, Vemco Ltd., Halifax, NS, Canada) measuring body temperature were inserted for 7 ± 1 d into the vaginal cavity of 31 dairy cows and programmed to take 1 reading/min. Milk temperature was recorded at each milking event by the AMS, and values from the vaginal loggers were paired with the corresponding MT. The correlation (r) between vaginal temperature (VT) and MT was 0.52. Vaginal temperature was higher (39.1 ± 0.4°C) than MT (38.6 ± 0.7°C) with a mean difference of 0.5 ± 0.6°C. The ability of MT to identify cows with fever was assessed using 2 approaches. In the first approach, VT could indicate fever at any time of the day, whereas MT could display fever only during the milking events of a given day. Different definitions of fever based on thresholds of VT and duration exceeding these thresholds were constructed. Different thresholds of MT were tested to distinguish between cows with and without fever. The combination of 39.0°C as a threshold for MT and 39.5°C for at least 2h/d as a threshold for VT resulted in the highest combination of sensitivity (0.65) and specificity (0.65). In the second approach, we evaluated whether MT could identify cows with fever at a given milking event. A threshold of MT >38.7°C delivered the best combination of sensitivity (0.77) and specificity (0.66) when fever was defined as VT ≥39.5°C. Therefore, MT measured by AMS can be indicative of fever in dairy cows to a limited extent.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".