Moose, Caribou, and Grizzly Bear Distribution in Relation to Road Traffic in Denali National Park, Alaska
Why this work is in the frame
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Bibliographic record
Abstract
Park managers are concerned that moose (Alces alces), caribou (Rangifer tarandus), and grizzly bears (Ursus arctos) may be avoiding areas along the 130 km road through Denali National Park as a result of high traffic volume, thus decreasing opportunities for visitors to view wildlife. A wildlife monitoring system was developed in 1996 that used 19 landscape level view sheds, stratified into four sections based on decreasing traffic along the road corridor. Data were collected from 22 samplings of all viewsheds during May-August in 1996 and 1997. In 1997, nine backcountry viewsheds were established in three different areas to determine whether density estimates for each species in the backcountry were higher than those for the same animals in similar road-corridor areas. Densities higher than those in the road corridor were found in one backcountry area for moose and in two backcountry areas for grizzly bears. None of the backcountry areas showed a higher density of caribou. We tested hypotheses that moose, caribou, and grizzly bear distributions were unrelated to the road and traffic. Moose sightings were lower than expected within 300 m of the road. More caribou and grizzly bears than expected occurred between 601 and 900 m from the road, while more moose and fewer caribou than expected occurred between 900 and 1200 m from the road. Bull moose in stratum 1 were distributed farther from the road than bulls and cows in stratum 4; cows in stratum 1 and bul1s in stratum 2 were distributed farther from the road than cows in stratum 4. Grizzly bears in stratum 2 were distributed farther from the road than bears in stratum 3. The distribution of moose sightings suggests traffic avoidance, but the spatial pattern of preferred forage may have had more of an influence. Caribou and grizzly bear distributions indicated no pattern of traffic avoidance.
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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 | 0.000 | 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 it