Comparison of sampling precision for nearshore marine wildlife using unmanned and manned aerial surveys
Bibliographic record
Abstract
Aerial surveys of large marine wildlife in nearshore areas can support management actions to ensure conservation of this megafauna. While most aerial surveys of marine wildlife have been carried out using manned aircraft, unmanned aerial systems (commonly known as drones) are being increasingly used. Here, we compare the relative accuracy and precision of marine wildlife surveys from a multirotor drone and a manned helicopter for the first time. At two locations on the east coast of Australia, we simultaneously surveyed sharks (including white sharks, Carcharodon carcharias), dolphins, rays, and sea turtles in nearshore coastal areas using a multirotor drone (DJI Inspire I) and a helicopter (Robinson 44 Clipper II) over 26 separate flights. Sampling included the real-time quantification of marine wildlife by an observer in the helicopter and the pilot of the drone. The video feed from the drone was then later re-sampled in the laboratory. Of the three methods, post-hoc analysis of drone video footage is likely to provide the most accurate and precise estimates of marine wildlife in nearshore areas. When real-time data are required (e.g., for shark-risk mitigation), manned helicopters (over larger stretches of coast) and drones (across localised beaches) will both be useful.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".