Estimating detection probability for Canada lynx <i>Lynx canadensis</i> using snow‐track surveys in the northern Rocky Mountains, Montana, USA
Bibliographic record
Abstract
We used snow‐tracking surveys to determine the probability of detecting Canada lynx Lynx canadensis in known areas of lynx presence in the northern Rocky Mountains, Montana, USA during the winters of 2006 and 2007. We used this information to determine the minimum number of survey replicates necessary to infer the presence and absence of lynx in areas of similar lynx density (approximately 2.8 lynx/100 km 2 ) with confidence. The probability of detecting lynx in mountainous habitats that support resident populations was 0.80‐0.99 when surveys were conducted on an 8 × 8 km 2 grid with 10 km of search effort per cell. Snow‐track surveys were highly successful at detecting the presence of Canada lynx over large landscapes. Two survey replicates established absence of Canada lynx with 95% certainty. The high probability of detection associated with snow‐track surveys makes this method useful for documenting populations of Canada lynx in areas where their status is uncertain.
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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 | 0.002 | 0.001 |
| 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".