Investigating thermal physiology in large whales via aerial infrared thermography
Bibliographic record
Abstract
The critically endangered status of North Atlantic right whales (NARWs, Eubalaena glacialis) warrants the development of new, less invasive technology to monitor the health of individuals. Combined with advancements in remotely piloted aircraft systems (RPAS, commonly “drones”), infrared thermography (IRT) is being increasingly used to detect and count marine mammals and study their physiology. We conducted RPAS-based IRT over NARWs in Cape Cod Bay, Massachusetts, USA in 2017 and 2018. Observations demonstrated three particularly useful applications of RPAS-based IRT to study large whales: 1) exploring patterns of cranial heat loss and providing insight into the physiological mechanisms that produce these patterns; 2) tracking subsurface individuals in real-time (depending on the thermal stratification of the water column) using cold surface water anomalies resulting from fluke upstrokes; and 3) detecting natural changes in superficial blood circulation or diagnosing pathology based on hot anomalies on post-cranial body surfaces. These qualitative applications present a new, important opportunity to study and monitor large whales, particularly rare and at-risk species like NARWs. Despite the challenges of using this technology in aquatic environments, the applications of RPAS-based IRT for monitoring the health and behavior of endangered marine mammals, including the collection of quantitative data on thermal physiology, will continue to diversify.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".