Evaluation of a drone to map and monitor critical habitat features for the Sage Thrasher (<i>Oreoscoptes montanus</i>)
Bibliographic record
Abstract
Habitat destruction is the leading cause of global biodiversity loss. To recover endangered species requires knowing the habitat elements that are essential to their survival, defined as critical habitat. This paper demonstrates the application of unoccupied aerial vehicles (UAVs), for the recovery of habitat for endangered species. We establish a replicable methodology for surveying and monitoring potentially suitable nest shrubs for Sage Thrashers ( Oreoscoptes montanus) using an Aeryon SkyRanger. We trialed the methodology in winter when operational demands are minimal and before the birds return to breed. We found drone image analysis aligned well with field assessments of Artemisia tridentata height and may present a cost-efficient approach for mapping and monitoring critical habitat features for nesting Sage Thrashers. The UAV classification accuracy was 100% for nest shrubs and 85% for non-nest shrubs ( p-value < 0.001). We found the UAV-based method over-estimated the height of shrubs compared to the field-based measurements, where the 80 cm threshold for a suitable shrub corresponded to 73 cm as measured in the field (regression slope of 1.43, with an adjusted R 2 = 0.75, p-value < 0.0001). The approach also holds promise for other sagebrush steppe species of conservation concern.
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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) | 0.000 | 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".