Drone-based infrared thermography to measure the intranasal temperature of baleen whales
Bibliographic record
Abstract
Traditional methods for quantifying the internal temperature of marine mammals require handling live animals, which is not practical for free-swimming baleen whales. Developing a less invasive, more repeatable method would significantly improve our understanding of whale health and thermal physiology. Infrared thermography (IRT) devices compatible with remotely piloted aircraft systems (RPAS) have facilitated qualitative assessments of heat signatures from marine mammals at sea, but absolute temperatures derived using this approach are rare. The goal of this study was to develop a precise empirical method for estimating intranasal temperatures of baleen whales using RPAS-based IRT. We conducted controlled field experiments and flights over North Atlantic right whales (Eubalaena glacialis, NARWs) to develop and test the methodology. Two approaches were evaluated to estimate intranasal temperatures from IRT sensor intensities: a three-point empirical line regression calibrated per flight using known-temperature objects and a generalized linear model incorporating environmental variables. Controlled field experiments demonstrated that the former approach had a median bias of −0.6°C (interquartile range: 1.5ºC), while the latter approach had unexplained negative proportional bias with increasing true temperature of the target object. After accounting for bias, the former approach yielded an average intranasal temperature of 26.9 ± 1.7°C for 21 unique NARWs. The anatomy of the mysticete upper respiratory tract and physiological heat conservation strategies may explain why estimates were low compared to internal temperatures measured from baleen whales using other techniques (30–39ºC). Variability within whales was less than ± 2°C, supporting the use of these methods to monitor the health of individuals over time. However, variability among whales was greater (up to 7ºC). Improvements in our understanding of whale physiology and respiratory mechanics and advancements in RPAS-based IRT calibrations could make this technology more reliable for assessing individual body temperatures and monitoring populations in the future.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".