Interrelationships and Interactions Between Helicopter Skiing, Forests and the Forestry Industry in British Columbia, Canada
Bibliographic record
Abstract
Helicopter skiing is intricately intertwined with forested landscapes and the forestry industry. The study objective was to determine, describe and analyze the manners in which helicopter skiing, forests and the forestry industry are related and how helicopter skiing interacts with forests and the forestry industry in British Columbia. This was accomplished by examining the available literature related to the use of Crown land for helicopter skiing and for forestry purposes, by gathering information from stakeholders via telephone interview and questionnaire, as well as by comparing snow profiles from open and forested sites. Helicopter skiing often takes place on land managed by the forestry industry for timber production. Forested slopes are desirable to skiers because they may have beneficial impacts on the quality of the snow pack for skiing. Operators of helicopter skiing businesses depend upon forested terrain to provide stable and therefore safe snow packs with regard to avalanches and to allow for safe air transport during poor visibility. Forested land is used more frequently for helicopter skiing in the coastal region of western British Columbia than in the interior or eastern portion of the province. Some helicopter skiing businesses have engaged in efforts to form multi-use management projects with forestry businesses; however the majority of such projects have been unsuccessful in attaining the needs of both parties. Potential exists for helicopter skiing operators and forestry businesses to effectively work together, although socio-economic influences prevent multi-use management of forested Crown land from taking place.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".