Daily variation in the udder surface temperature of dairy cows measured by infrared thermography: Potential for mastitis detection
Bibliographic record
Abstract
The daily and within-day variation in udder temperature was monitored in dairy cows (n = 10) using infrared thermography (IRT). The initial assessment and prediction of udder surface temperature variation would hopefully form the basis for future development of an early detection method for mastitis. Our initial objective was to determine the magnitude and pattern of udder temperature variation. To accomplish this, we measured daily fluctuations in udder temperature and the influence of environmental factors upon these values in non-mastitic cows. Udder temperature rose significantly after an exercise period (P < 0.05). Within-day monitoring of udder temperature demonstrated there was a distinct circadian rhythm. Lag regression analysis showed that previous daily udder temperatures together with environmental temperature parameters could successfully predict current udder temperature with a high degree of accuracy. The variation between predicted and actual udder temperature was within the detectable range for an inflammatory response. Infrared thermography shows promise in its application if coupled with environmental temperature monitoring as an early detection method for mastitis. Key words: Thermography, dairy cattle, environment, temperature
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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) | 0.000 | 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 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".