A Low-Cost Technique for Radio-Tracking Wildlife Using a Small Standard Unmanned Aerial Vehicle
Bibliographic record
Abstract
Recent advances in using unmanned aerial vehicles (UAVs) to study wildlife offer promise and may improve data collection efficiency, and small UAVs, such as multirotor platforms, are suitable for this task because they are easy to deploy, can fly over terrain that is difficult to access on foot, and can be programmed to follow specific trajectories. The objective of our study was to determine whether a small UAV could be outfitted with a radio receiver to pick up signals from radio-transmitters worn by small forest birds (Catharus bicknelli and C. ustulatus). We compared radio-monitoring using an UAV and a ground-based vehicle. The detection of over 50% of the tagged birds in the 50 m altitude flights is indicative of the real potential of the concept. This is supported by a signal strength significantly stronger and more constant than ground-based signals. The signal receptor experienced no significant interference from the UAV electronics, thus enabling a “clean” set of detections from the birds. Based on these preliminary results, we conclude that UAVs can yield useable data from animals wearing light-weight transmitters. Radio-tracking birds with UAVs presents strong potential for applications in all types of forest stands, or even in the radio-tracking of multiple species or taxa.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".