Modeling Wolverine Occurrence Using Aerial Surveys of Tracks in Snow
Bibliographic record
Abstract
ABSTRACT We designed a novel approach to determining extent of distribution and area of occupancy for wolverines ( Gulo gulo ) by using aerial surveys of tracks in snow and hierarchical spatial modeling. In 2005 we used a small, fixed‐wing aircraft with pilot and one observer to search 575 of 588 survey units for wolverine tracks in approximately 60,000 km 2 of boreal forest in northwestern Ontario, Canada. We used sinuous flight paths to scan open areas in the forest in the 100‐km 2 survey units. We detected tracks in 138 (24%) of the 575 sampled units. There was strong evidence of occurrence (probability of occurrence >0.80) in 30% of the 588 survey units, weak evidence of occurrence (0.50–0.80) in 12%, weak evidence of absence (0.20–0.50) in 15%, and strong evidence of absence (< 0.20) in 43%. Wolverine range comprised 59% of the study area and area of occupancy was 33,400 km 2 . With information on probability of occurrence and core areas of occupation for wolverines in our study area, resource managers and others can examine factors that influence wolverine distribution patterns and use this information to formulate best management practices that will maintain wolverines on the landscape in the face of increasing resource development. Comparing future survey results with those of our 2005 survey will provide an objective way to assess the efficacy of management practices.
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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 | 0.003 | 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".