Early detection and prediction of infection using infrared thermography
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
Early detection and/or prediction of disease in an animal is the first step towards its successful treatment. The objective of this study was to investigate the capability of infrared thermography as a non-invasive, early detection method for identifying animals with a systemic infection. A viral infection model was adopted using 15 seronegative calves whose body weight averaged 172 kg. Ten of these calves were inoculated with Type 2 bovine viral diarrhoea virus (strain 24515) and five were separately housed and served as uninfected controls. A simultaneous comparison of infrared characteristics in both infected and control animals was conducted over approximately 15 d. In addition, measures of blood and saliva cortisol, immunoglobulin A, blood haptoglobin and clinical scores were obtained. Infrared temperatures, especially for facial scans, increased by 1.5°C to over 4°C (P < 0.01) several days to 1 wk before clinical scores or serum concentrations of acute phase protein indicated illness in the infected calves. The data suggest that infrared thermal measurements can be used in developing an early prediction index for infection in calves. Key words: Infection, early detection, infrared thermography, cattle
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it