Mitigating avian collision with power lines: a proof of concept for installation of line markers via unmanned aerial vehicle
Bibliographic record
Abstract
Avian collisions with overhead power lines are a global conservation concern. Collisions are mitigated primarily through marking power lines to increase their visibility. Line marking is typically accomplished via an expensive and potentially dangerous process of hovering a helicopter within 1 m of a wire and installing line markers by hand. Unmanned Aerial Vehicles (UAVs) may offer a less dangerous, less costly alternative that is also less disturbing to wildlife. Herein we describe equipping a commercially available UAV with an installation arm designed in collaboration with the Colorado State University Department of Mechanical Engineering to install line markers on a power line. The UAV installed line markers within a 30 cm target window on a model power line. The proof of concept described here demonstrates the potential utility of UAVs in mitigating avian collision with overhead power lines.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".