Mapping interactions between winter recreationists and an endangered ungulate
Bibliographic record
Abstract
Abstract Southern mountain caribou ( Rangifer tarandus caribou ) inhabit the interior mountain ranges of British Columbia, Canada. This population of woodland caribou is federally designated as threatened owing primarily to predation and habitat loss, but other compounding factors may impede their recovery. Of note are potential impacts from heli‐skiing, a form of winter recreation that uses helicopters to transport skiers in wilderness areas. During late‐winter, southern mountain caribou become range resident in high‐elevation, old‐growth forests and subalpine parklands. The deep snow characteristic of late‐winter habitat offers reduced encounters with predators, and abundant arboreal lichens on which to feed; however, heli‐skiing also occurs in these areas. Whether heli‐skiing has any demographic impacts on caribou is unknown, but previous work has shown that heli‐skiing can elicit short‐term flight responses and longer‐term reductions in space use and elevated stress levels. Furthermore, little is known about where skiing occurs or where helicopters transport skiers between lodges and ski areas, leaving regulating bodies with little information to guide management recommendations. We paired anonymized fitness tracker user data from the rasterized Strava global heatmap with 4 years of caribou global positioning system (GPS) location data to identify hotspots of potential interactions between heli‐skiers and caribou. There were approximately 400 km 2 where the potential for conflict appeared high out of 3,116 km 2 suitable for heli‐skiing. The majority of heli‐ski operators have the capacity to reduce their interactions with caribou to almost zero through avoidance of key habitats and timing of use of that terrain. We recommend that heli‐ski operators work with governmental managers towards the optimal use of tenures through rolling closures or the permanent diversion of ski runs away from high potential conflict areas.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".