Bibliographic record
Abstract
Abstract Recent studies have begun to address the importance of weather information for leisure activities. This paper contributes to the understanding of how weather information is sourced, perceived, and used for the discretionary and weather-dependent winter activities of skiing, snowboarding, and snowmobiling. A survey of 1948 Ontario (Canada) skiers/snowboarders and snowmobilers is the empirical basis for the paper, providing insights into how winter recreationists are both similar to and different from the general public with respect to weather information. Results show that virtually all (≥97%) skiers/snowboarders and snowmobilers use weather forecasts when planning an outing, which are primarily (≥95%) sourced through Internet and mobile devices. Skiers/snowboarders and snowmobilers are also highly attentive to rain and freezing rain variables in the forecast, as it negatively affects participation. The results also demonstrate the importance of forecast use for planning travel to snow resorts and snowmobile trails, with poor road conditions likely to result in a postponed or cancelled trip. These findings underscore the differing weather needs of subpopulations, with the need for continued research to examine variations among weather forecast users for context specific decision making.
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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".