The Effect of Vehicle Traffic on Wildlife in Denali National Park
Bibliographic record
Abstract
We recorded observations of caribou (Rangifer tarandus), grizzly bear (Ursus arctos), Dall sheep (Ovis dalli) and moose (Alces alces) along the Denali National Park and Preserve road corridor during 1995-97. We compared these observations to similar data from previous studies to evaluate the effect of an increase in traffic on the number of animals sighted and their behavior. Between 1972 and 1997, annual visitation to Denali National Park increased from about 45000 to 350000, with attendant increases in traffic on the park road. The mean number of caribou, grizzly bear, and Dall sheep observed did not decline (p > 0.301) from 1973 to 1997. The number of moose observed declined by more than 50% (R² = 0.529, p < 0.001). The estimated population of moose also declined over the same period (R² = 0.374, p = 0.002). The distance from the park road at which caribou and grizzly bears were sighted did not change (p > 0.787), but fewer moose (p < 0.031) were observed within 100 m of the road and fewer sheep (p < 0.011) were observed between 400 and 500 m from the road. Adverse behavioral responses to traffic (e.g., running from vehicles) occurred in less than 1.3% of observations for each species. Increased traffic on the park road apparently has not caused significant changes in abundance, distribution, or behavior of caribou, grizzly bear, Dall sheep, and moose in the park road corridor.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".