Performance of manned and unmanned aerial surveys to collect visual data and imagery for estimating arctic cetacean density and associated uncertainty
Bibliographic record
Abstract
Manned aerial surveys have been used successfully for decades to collect data to infer cetacean distribution, density (number of whales/km2), and abundance. Unmanned aircraft systems (UAS) have potential to augment or replace some manned aerial surveys for cetaceans. We conducted a three-way comparison among visual observations made by marine mammal observers aboard a Turbo Commander aircraft; imagery autonomously collected by a Nikon D810 camera system mounted to a belly port on the Turbo Commander; and imagery collected by a similar camera system on a remotely controlled ScanEagle®UAS operated by the US Navy. Bowhead whale density estimates derived from the marine mammal observer data were higher than those from the Turbo Commander imagery; comparisons to the UAS imagery depended on survey sector and analytical method. Beluga density estimates derived from either dataset collected aboard the Turbo Commander were higher than estimates derived from the UAS imagery. Uncertainties in density estimates derived from the marine mammal observer data were lower than estimates derived from either imagery dataset due to the small sample sizes in the imagery. The visual line-transect aerial survey conducted by marine mammal observers aboard the Turbo Commander was 68.5% of the cost of the photo strip-transect survey aboard the same aircraft and 9.4% of the cost of the UAS survey.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".