Shark detection probability from aerial drone surveys within a temperate estuary
Bibliographic record
Abstract
Drones are easy to operate over metres-to-kilometre scales, making them potentially useful to monitor species distributions and habitat use in shallow estuaries with widely varying environmental conditions. To investigate the utility of drones for surveying bonnethead sharks (Sphyrna tiburo) across estuarine environmental gradients, we deployed decoys, fashioned to mimic sharks, in the field. Decoys were placed in two flight areas (0.8 km2each) in shallow (<2 m) water near Beaufort, N.C., on five days during 2015–2016. Survey flights were conducted using a fixed-wing drone (senseFly eBee) equipped with a digital camera. Images were indexed for combinations of six environmental factors across flights. Images representative of all (N = 36) observed environmental combinations were sent to a group of 15 scientists who were asked to identify sharks in each image. Non-parametric rank-sum comparisons and regression tree analysis on resultant detection probabilities highlighted depth as having the largest, statistically reliable influence on detection probabilities, with decreasing detection probabilities at increased depth. Detection probabilities were higher during midday flights, with notable effects of wind speed and cloud presence also apparent. Our study highlights depth as a first-order factor constraining the temperate estuarine habitats over which drones may reliably quantify sharks (i.e., <0.75 m).
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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 | 0.001 | 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) | 0.001 | 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".