Nest defense behaviour of four raptor species (osprey, bald eagle, ferruginous hawk, and red-tailed hawk) to a novel aerial intruder – a small rotary-winged drone
Bibliographic record
Abstract
A small rotary-winged unmanned aerial vehicle (UAV) was flown above the nests of four raptor species: osprey (Pandion haliaetus), bald eagle (Haliaeetus leucocephalus), ferruginous hawk (Buteo regalis), and red-tailed hawk (B. jamaicensis) to document the parental nest defense response to the aircraft. Adult behaviour was documented with a voice recorder and an ethogram, starting ~100 m distant from the nest and continuing until the base of the nest was reached, the survey completed, and the nest area exited. All adult movements and vocalizations were recorded with distance of bird and researchers from the nest when a given behaviour occurred. Ospreys showed the strongest nest defense response followed by ferruginous hawks and red-tailed hawks with bald eagles showing the least aggressive response. Ospreys showed no greater response to the UAV in the air near the nest than to researchers simply standing at the base of the nest structure, while bald eagles showed a significantly higher response to the aircraft than researchers at the nest base. Although aggression varied, no species showed aggression at levels that would discourage the use of UAVs to survey raptor nests. When a proper flight technique is adopted, UAVs can offer a useful tool for surveying raptor nests.
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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 | 0.000 |
| 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) | 0.001 | 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".