Infrared thermography detects febrile and behavioural responses to vaccination of weaned piglets
Bibliographic record
Abstract
An automated, non-invasive system for monitoring of thermoregulation has the potential to mitigate swine diseases through earlier detection. Measurement of radiated temperature of groups of animals by infrared thermography (IRT) is an essential component of such a system. This study reports on the feasibility of monitoring the radiated temperature of groups of animals as a biomarker of immune response using vaccination as a model for febrile disease. In Study A, weaned pigs were either treated with an intramuscular vaccine (FarrowSure Gold), a sham injection of 0.9% saline or left as untreated controls. An infrared thermal camera (FLIR A320) was fixed to the ceiling directly above the pen of animals, and recorded infrared images of the treatment groups at 5 min intervals. The effect on temperature of the spatial distribution of pigs within the pen was significant, with higher temperatures recorded when pigs were grouped together into a single cluster. A higher frequency of clustering behaviour was observed in vaccinated animals compared with controls during a period of the afternoon ~4 to 7 h post-vaccination. The daily mean of the maximum image temperature was significantly higher in vaccinated animals compared with control and sham-treated animals. In the vaccination treated group, the 24 h mean of the maximum temperature was significantly higher during the post-vaccination period compared with the 24 h period before vaccination. Increased temperature in the vaccinated animals occurred from ~3 h, peaked at ~10 h, and remained elevated for up to 20 h post-vaccination. In Study B, the effect of prevalence was tested in terms of the difference in maximum temperature between control and vaccination days. A thermal response to vaccination was detected in a pen of 24 to 26 animals when <10% of the animals were vaccinated. The results support the concept of radiated temperature measurements of groups of animals by IRT as a screening tool for febrile diseases in pig barns.
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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 | 0.000 |
| 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".