Monitoring Thermoregulation Patterns in Asian Elephants (Elephas maximus) in Winter Months in Southwestern Ontario Using Infrared Thermography
Bibliographic record
Abstract
Given the current and future threats to Asian elephants (Elephas maximus), maintaining a sustainable ex situ population is crucial for the longevity of the species. Using Infrared Thermography (IRT), thermoregulation of Asian elephants at low ambient temperatures was examined. Thermal images were taken at 15 min intervals over 60–90-min observation periods, once weekly, during January and February 2022. A total of 374 images were examined from 10 Asian elephants, which varied from 1 to 56 years of age. Data from thermograms of the ear and body were interpreted in view of weight, age and behavior. Variability in surface temperature was found most frequently in the ears, occasionally presenting as thermal windows—areas with dense underlying blood supply that aid in heat exchange. Thermal windows occurred most frequently in the distal, then medial, regions of the ear. The pattern of appearance of thermal windows in the ear provides support that the increase of blood flow is utilized as a method of warming. This preliminary study provides key insight into Asian elephant thermoregulation, suggesting that the species may be more well-adapted to lower ambient temperatures than previously thought.
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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 | 0.001 | 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) | 0.001 | 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".