Narwhal (<i>Monodon monoceros</i>) detection by infrared flukeprints from aerial survey imagery
Bibliographic record
Abstract
Abstract Visual and observer aerial surveys are important for monitoring wildlife populations but are subject to visibility biases where animals may go undetected. The use of infrared technology in aerial surveys has the potential to reduce visibility biases, both when recording data and in the retrospective processing of the footage, and thus complements visible wavelength photography. We used infrared video during marine mammal surveys in the high‐Arctic and indirectly detected narwhal (Monodon monoceros) via their thermal flukeprints (i.e., thermo‐stratified water mixing from fluke strokes). This novel indicator persisted for a longer duration than when the animal was at the water's surface, which likely improved the probability of an animal being observed by increasing the duration of its detectability. Using infrared to complement aerial photographic surveys may assist in monitoring whales, especially in remote areas. Our results highlight how infrared technology may be used to develop automatic detection and remote‐monitoring methodology.
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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 | 0.001 | 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".