Scaling the Laws of Thermal Imaging–Based Whale Detection
Bibliographic record
Abstract
Abstract Marine mammals are under growing pressure as anthropogenic use of the ocean increases. Ship strikes of large whales and loud underwater sound sources including air guns for marine geophysical prospecting and naval midfrequency sonar are criticized for their possible negative effects on marine mammals. Competent authorities regularly require the implementation of mitigation measures, including vessel speed reductions or shutdown of acoustic sources if marine mammals are sighted in sensitive areas or in predefined exclusion zones around a vessel. To ensure successful mitigation, reliable at-sea detection of animals is crucial. To date, ship-based marine mammal observers are the most commonly implemented detection method; however, thermal (IR) imaging–based automatic detection systems have been used in recent years. This study evaluates thermal imaging–based automatic whale detection technology for its use across different oceans. The performance of this technology is characterized with respect to environmental conditions, and an automatic detection algorithm for whale blows is presented. The technology can detect whales in polar, temperate, and subtropical ocean regimes over distances of up to several kilometers and outperforms marine mammal observers in the number of whales detected. These results show that thermal imaging technology can be used to assist in providing protection for marine mammals against ship strike and acoustic impact across the world’s oceans.
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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 | 0.016 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".