The use of infrared thermography to noninvasively measure the surface temperature of polar bears during bouts of social play
Bibliographic record
Abstract
Infrared thermography or thermal imagery is a noninvasive tool that can be used to measure the temperature of surfaces. Typically, thermal imagery is used for construction or military purposes but is increasingly used as a noninvasive tool in wildlife studies. We investigated the use of thermal imagery to measure surface temperature changes as a proxy for energetic expenditure. We measured the surface temperature of polar bear (Ursus maritimus) eyes, while immobilized, to determine whether the eye is a thermal window that can accurately indicate internal temperature. We found a significant difference (2.68 ± 0.41°C) between the surface temperature of the eye measured with thermal imagery and the internal rectal temperature. Additionally, we measured surface temperature changes in polar bears after bouts of social play as a proxy for energy expenditure. Mean temperature of the eye increased by 1.34 ± 0.43°C after social play, indicating that this activity increased energy expenditure. During the fasting season, polar bears rely on fat stores, and any energy expenditure beyond what is required to travel may be costly to their survival, particularly in years of low resource availability. We conclude that thermal imagery is a useful tool to noninvasively investigate the energetics of social play.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".