Infrared thermography as a non-invasive method for detecting fear-related responses of cattle to handling procedures
Bibliographic record
Abstract
Abstract Two experiments were conducted to determine whether maximum eye temperature, measured using infrared thermography (IRT), could be a non-invasive technique for detecting responses of cattle to handling procedures. Experiment one used six crossbred heifers randomly assigned to two groups in a crossover design and subjected to i) being hit with a plastic tube on the rump and ii) being startled by the sudden waving of a plastic bag. Experiment two used 32 crossbred bulls randomly assigned to three treatments: i) control, restraint only; ii) electric prod, two brief applications of an electric prod or, iii) startled, as in experiment one, accompanied by shouting. Exit speed (m s−1) was recorded on release from the restraint. Maximum eye temperature was recorded continuously pre- and post-treatment. In experiment one, eye temperature dropped rapidly between 20 and 40 s following both treatments and returned to baseline between 60 and 80 s following hitting and between 100 and 120 s following startling. In experiment two, eye temperature dropped between 0 and 20 s, following both treatments, and returned to baseline by 180 s, following startling plus shouting, but did not return to baseline for five minutes following electric prod. Exit speed tended to be faster following the electric prod. In conclusion, IRT detected responses that were due possibly to fear and/or pain associated with the procedures and may therefore be a useful, non-invasive method for assessing aversiveness of handling practices to cattle.
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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 | 0.001 | 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 | 0.001 |
| 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".