the central Rocky Mountains. Journal of Wildlife Management 63:1094-1108. The
Bibliographic record
Abstract
authors studied the dispersal of wolves in Glacier National Park, Montana from 1979-1997. Wolves tended to disperse to the north toward areas of greater wolf density and less to the south, towards areas of higher rural human activity. Human-caused mortality was highest near roads. James, A., and S. Smith. 2000. Distribution of caribou and wolves in relation to linear corridors. Journal of Wildlife Management 64:154-159. Investigators used telemetry to examine the distribution of caribou and wolves in northeastern Alberta. Caribou tended to avoid human-made corridors, while wolves occasionally utilized human-made corridors. Wolf predation occurred at higher rates near corridors such as roads. The authors conclude that corridors may cause increased predation. Mech, L. D. 1989. Wolf population survival in an area of high road density. American Midland Naturalist 121:387-389. Mech radiocollared and studied seventy-one wolves in the Superior National Forest, Minnesota from 1969-1986. Mortality in an area with road density of.73 km/km2 was 69 % but there was no human-caused mortality in a bordering roadless area. The author concludes that wolf populations can survive in an area of high road density if roadless sanctuaries are nearby.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".