Determinants of Gray Wolf (<i>Canis lupus</i>) Sightings in Denali National Park
Bibliographic record
Abstract
Wildlife viewing within protected areas is an increasingly popular recreational activity. Management agencies are often tasked with providing these opportunities, yet quantitative analyses of factors influencing wildlife sightings are lacking. We analyzed locations of GPS-collared wolves and wolf sightings from 2945 trips in Denali National Park and Preserve, Alaska, USA, to provide a mechanistic understanding of how viewing opportunities are influenced by attributes of wolves and physical, biological, and harvest characteristics. We found that the presence of masking vegetation, den site proximity to the road, pack size, and presence of a wolf harvest closure adjacent to the park affected wolf sightings, and the influence of den proximity on sightings depended on harvest management. Wolf sightings increased with den site proximity to the road in years with a harvest closure adjacent to the park but not in the absence of the closure. The effect of the harvest closure on sightings was similar in magnitude to an increase in pack size by two wolves or a more than a two-fold decrease in masking vegetation. These findings were consistent across a 10-fold change in spatial resolution. Quantitative analysis of the factors influencing wildlife sightings provides valuable insight for agencies tasked with managing viewing opportunities.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".