Infrared thermography as a non-invasive tool to study animal welfare
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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 it