Infrared thermography as a non-invasive tool to study animal welfare
Bibliographic record
Abstract
Abstract Growing public concern regarding animal welfare and consumer demand for humanely produced products have placed pressure on the meat, wool and dairy industries to improve and confirm the welfare status of their animals. This has increased the need for reliable methods of assessing animal welfare during commercial farm practices. The measurement of the stress caused by commercial farm practices is a major component of animal welfare assessment. However, a major issue for animal welfare science is that many of the techniques used to measure stress involve invasive procedures, such as blood sampling, which may themselves cause a stress response and therefore affect the measurement of interest. To reduce this problem, a number of non-invasive or minimally invasive methods and devices have been developed to measure stress. These include the measurement of cortisol concentrations in saliva and faeces, and remote devices for recording body temperature, heart rate and the collection of blood samples. This review describes the benefits and limitations of some of these methods for measuring stress. In particular, the review focuses on recent advances and current research in the use of infrared thermography (IRT) for measuring stress. Specific applications for IRT in the dairy and beef industries are also described including an automated, non-invasive system for early diagnosis of infection in cattle. It is essential that non-invasive measures of acute and chronic stress are developed for reliable assessment of animal welfare during standard farm management practices and IRT may be a useful tool for this purpose. IRT may offer advantages over many other non-invasive systems as it appears to be capable of measuring different components of the stress axis, including acute sympathetic and hypothalamic-pituitary-adrenocortical responses.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".