A low cost way for assessing bird risk hazards in power lines: Fixed-wing small unmanned aircraft systems
Bibliographic record
Abstract
Accidents on power lines are one of the most important causes of man-induced mortality for raptors and soaring birds. The factors that condition the hazard have been extensively studied, and currently there are a variety of technical solutions available to mitigate the risk. Most of the resources in conservation projects to reduce avian mortality now are invested in fieldwork to monitor the lines, which diverts the resources available to install actual corrective measures to mitigate bird hazard. Little progress has been achieved in the methodology to characterize line risk, which is an expensive, tedious, and time-consuming task. In this work we describe the use of low cost small unmanned aircraft systems (sUAS) equipped with on-board cameras for power line surveillance. As a case study, we characterized four power lines, geo-referenced every pylon in selected portions, and assessed their hazard for birds. We compare the effectiveness of two variants of the sUAS method for data acquisition and two methods of plane control. This work provides evidence of the usefulness of sUAS as a fast, inexpensive, and practical tool in conservation biology, adding to their already known applications in wildlife monitoring, the environmental impact assessment of infrastructures.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".