Proper flight technique for using a small rotary-winged drone aircraft to safely, quickly, and accurately survey raptor nests
Bibliographic record
Abstract
Small rotary-winged unmanned aerial vehicles or “drones” mounted with a small video camera were successful in surveying the nest contents of four species of raptor, including Osprey (Pandion haliaetus), Bald Eagle (Haliaeetus leucocephalus), Ferruginous Hawk (Buteo regalis), and Red-tailed Hawk (B. jamaicensis) in an accurate and safe manner when the proper flight technique was employed. A total of 110 surveys were completed in 2013 and 2014 with quality images of nest contents obtained in 106 or (96.4%) of flights. A successful and safe flight requires two personnel: the pilot who controls the aircraft and the spotter who monitors the behaviour of the adult birds defending the nest and keeps the pilot updated on all potentially dangerous interactions between aircraft and the birds. With the video camera recording, the aircraft is flown above the nest to a predetermined location that allows an unobscured camera shot of the nest. This technique can be readily adapted to a variety of habitat types and species. The accuracy of data obtained combined with the flexibility, low cost, and speed of this technique make it a useful technological alternative to the safety risks and obtrusiveness associated with traditional survey techniques.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".