Flying beneath the clouds at the edge of the world: using a hexacopter to supplement abundance surveys of Steller sea lions (<i>Eumetopias jubatus</i>) in Alaska
Bibliographic record
Abstract
Aerial imagery is the most effective method National Marine Fisheries Service (NMFS) uses to assess abundance of Steller sea lions (Eumetopias jubatus). These images are traditionally captured from occupied aircraft, but the long distances between airfields along the 1900 km Aleutian Island chain, inclement weather during the survey season, and dangerous winds at sites adjacent to cliffs severely limit flying opportunities. Because of the pressing need for current trend information for a population in persistent decline we turned to a small unoccupied aircraft system (UAS), an APH-22 hexacopter. Our primary objective was to supplement traditional aerial surveys during the annual abundance survey. The second objective was to test whether the resolution of images captured with the hexacopter was adequate for sighting permanently marked individuals. From June to July 2014, NMFS biologists based on a research vessel assessed sites from Attu Island to the Delarof Islands (n = 23), surveying sites from land (n = 12) and with the hexacopter (n = 11). Simultaneously, traditional aerial surveys were conducted east of the Delarof Islands (n = 172). This combined approach enabled us to conduct the most complete survey of adult, juvenile, and newborn Steller sea lions in the Aleutian Islands since the 1970s. Images collected also allowed for us to identify alpha-numeric permanent marks on individuals as small as juveniles. With this successful implementation of UAS, NMFS plans to use the hexacopter to supplement future surveys.
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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 | 0.001 |
| 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 | 0.001 |
| 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".