Estimating wolverine<i>Gulo gulo</i>population size using quadrat sampling of tracks in snow
Bibliographic record
Abstract
Low densities and wide-ranging behaviour make wolverines Gulo gulo difficult to monitor. We used quadrat sampling of tracks in snow to estimate wolverine populations. We conducted aerial surveys in upper Turnagain Arm and the Kenai Mountains (TAKM) in south-central Alaska and in Old Crow Flats (OCF) in northern Yukon during March 2004 following procedures for the sample-unit probability estimator (SUPE). This technique uses network sampling of tracks in snow in a stratified random system of quadrats or sample units. In TAKM, we sampled 87 (51%) out of 171 quadrats within a survey area of 4,340 km2. The estimated density was 3.0 (± 0.4 SE) wolverines/1,000 km2 with a coefficient of variation (CV) of 12.0%. In OCF, we sampled 96 (71%) out of 135 quadrats within a survey area of 3,375 km2. The estimated density was 9.7 (± 0.6 SE) wolverines/1,000 km2 with a CV of 6.5%. Our results indicated that the SUPE technique is an efficient method of obtaining precise estimates of wolverine population size under markedly different environmental conditions and population densities. We suggest that, where practical, it may be a less labour-intensive and more cost-effective technique for estimating wolverine abundance compared with techniques that do not use probability sampling of tracks.
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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".