Evaluation of a ship-based unoccupied aircraft system (UAS) for surveys of spotted and ribbon seals in the Bering Sea pack ice
Bibliographic record
Abstract
The remote pack ice of the arctic and subarctic seas is challenging to access, yet extremely important to understand and monitor. The pack ice holds the key to understanding ecosystem responses to climate change and is vital habitat for many species including ice-associated seals. Unoccupied aircraft systems (UAS) are a new class of tools that may overcome the traditional challenges associated with expansive offshore surveys. We conducted UAS flights over the pack ice during a spring 2009 National Oceanic and Atmospheric Administration (NOAA) cruise to the Bering Sea to determine whether advances in UAS technology can enable effective large-scale, systematic ship-based surveys for seals in the seasonal ice of the Bering, Beaufort, and Chukchi Seas. A fixed-wing ScanEagle UAS was successfully launched and recovered from the NOAA ship McArthur II to conduct small-scale transect surveys up to 5 nautical miles (M) from the ship's position. More than 27 000 images were collected from 10 flights over the Bering Sea pack ice and seals were identified in 110 of these images. Review of the images indicated a marked reduction in disturbance to seals when compared to images collected from occupied, low-altitude helicopter surveys. These results suggest that large-scale UAS surveys of arctic and subarctic habitat in United States airspace will be possible with improvements in technology, reduced operational costs, and the establishment of inclusive airspace regulations.
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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 | 0.002 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".