Validation of Anatomical Sites for the Measurement of Infrared Body Surface Temperature Variation in Response to Handling and Transport
Bibliographic record
Abstract
This study aimed at validating the anatomical sites for the measurement of infrared (IR) body surface temperature as a tool to monitor the pigs’ response to handling and transport stress. The selected anatomical sites were the neck (infrared neck temperature—IRNT), rump (infrared rump temperature—IRRT), orbital (infrared orbital temperature—IROT) and behind ears (infrared behind ears temperature—IRBET) regions. A total of 120 pigs were handled from the finishing pen to the loading dock through a handling test course. Two handling types (gentle vs. rough) and number of laps (1 vs. 3) were applied according to a 2 × 2 factorial design. After loading, pigs were transported for 40 min and returned to their home pens. Animal behavior, heart rate, rectal temperature and salivary cortisol concentration were measured for validation. Increased IR body temperature, heart rate and salivary cortisol levels were observed in response to rough handling and longer distance walk (P < 0.05 for all). The greatest correlations were found between IROT and IRBET temperatures and salivary cortisol concentration at the end of the handling test (r = 0.49 and r = 0.50, respectively; P < 0.001 for both). Therefore, IR pig’s head surface temperature may be useful for a comprehensive assessment of the physiological response to handling and transport stress.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".