Terrain park feature compliance with Québec ski area safety recommendations
Bibliographic record
Abstract
OBJECTIVES: The primary objective of this paper is to examine terrain park (TP) feature compliance with recommendations from a ski area industry guide (are TP features compliant with the guide?) and determine factors that could be associated with TP feature compliance in Québec ski areas (do factors influence TP feature compliance?), Canada. These recommendations on the design, construction and maintenance are provided by the Québec Ski Areas Association Guide. METHODS: A group of two to four trained research assistants visited seven ski areas. They used an evaluation tool to assess the compliance of 59 TP features. The evaluation tool, originally developed to assess the quality of TP features based on the guide, was validated in a previous study. Compliance was calculated by the percentage of compliant measures within a given feature. The potential influence of four factors on compliance (size of the TP, size of the feature, snow conditions and type of feature) were examined using a mixed-effects logistic regression model. RESULTS: The average TP feature compliance percentage was 93% (95% CI 88% to 99%) for boxes, 91% (95% CI 89% to 94%) for rails and 89% (95% CI 86% to 92%) for jumps. The logistic regression showed that none of the four factors examined were associated with TP feature compliance with the guide. CONCLUSION: Our results suggest that TP features are highly compliant with the guide in Québec ski 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 | 0.003 | 0.015 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".