Evaluating UAV-based techniques to census an urban-nesting gull population on Canada’s Pacific coast
Bibliographic record
Abstract
The use of unmanned aerial vehicles, or drones, in wildlife monitoring has increased in recent years, particularly in hard-to-access habitats. We used fixed-wing and quadcopter drones to census an urban-nesting population of Glaucous-winged Gulls in Victoria, Canada. We conducted our study over 2 years and asked whether (i) drones represent a suitable survey method for rooftop-nesting gulls in our study region; and (ii) Victoria’s urban gull population had increased since the last survey >30 years earlier. Using orthomosaic imagery derived from drone overflights, we estimated at least a threefold increase over the 1986 count reported for the entire city (from 114 to 346 pairs), and an approximate tenfold increase in the number of gulls nesting in the downtown core. Drones proved to be an excellent platform from which to census rooftop-nesting birds: occupied nests were readily discernible in our digital imagery, and incubating birds were undisturbed by drones. This lack of disturbance may be due to Victoria’s location in an aerodrome; gulls experience dozens of floatplane and helicopter flights per day and are likely habituated to air traffic. Glaucous-winged Gulls have declined considerably at their natural island colonies in the region since the 1980s. Our results indicate that although urban roofs provide replacement nesting habitat for this species, local gull populations have not simply relocated en masse from islands to rooftops in the region.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".