Unmanned aircraft systems enable three-dimensional viewshed-based assessment of potential disturbance to nesting raptors by recreational rock climbing
Bibliographic record
Abstract
Unmanned aircraft systems (UAS) can be safer, less expensive, and less labor intensive than manned aircraft in wildlife conservation programs. Consequently, the use of UAS is increasing, but other than installation of line markers to reduce avian collision with power lines, UAS approaches generally involve passive observations. We wondered if UAS could more actively help guide conservation decision-making, so we used UAS-sourced photographs to create 3D models of cliffs to conduct viewshed-based assessments of potential disturbance to nesting raptors by recreational rock climbing. At Cathedral Spires Park and Clear Creek Canyon Park in Jefferson County, Colo., we collected 4790 photographs from which we constructed 3D models. We identified climbing routes and climbing areas with potential to disturb nesting Peregrine Falcons (Falco peregrinus) and Golden Eagles (Aquila chrysaetos). Our findings were useful in providing Jefferson County Open Space (JCOS) with quantitative data so that informed defensible resource management decisions could be made. This project provides an example of how UAS can be used to actively create products useful in wildlife conservation and management and, given the widespread and increasing popularity of rock climbing globally, likely can be generalized to other areas worldwide where rock climbers and nesting raptors share cliffs.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".