Using drones to measure the status of cavity-nesting raptors
Bibliographic record
Abstract
Many wildlife species, including hole-nesting raptors, occupy cavities that are challenging or unsafe for observers to examine. We conducted field tests using an inexpensive, off-the-shelf camera attached to an affordable commercial drone to assess the reproductive success of American kestrels ( Falco sparverius), a small, cavity-nesting raptor. Specifically, we developed a system that consisted of (1) a wireless mini camera transmitting images to an observer’s phone and (2) a Mini-Mavic 2 drone controlled through direct line-of-sight piloting. Following successful preliminary flights involving chicken eggs and other inanimate objects, we field-tested the system in two kestrel nest boxes in the wild. The system accurately recorded the contents of all nest boxes during the trials, provided the wind speed remained below approximately 2.5 m/s (5 knots). However, wind speeds at this level were observed only 40% of the days in our study area during kestrel breeding, which limited the surveying opportunities. Our system offers a promising method for surveying wildlife inhabiting inaccessible cavities.
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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".