Snowmobiling and Climate Change: Exploring Shifts in Snowmobile Activity Using a Temporal Analogue Approach in Ontario (Canada)
Bibliographic record
Abstract
The multi-billion-dollar snowmobile industry is predicated on natural snowfall and cold temperatures, with a near absence of research that examines industry response to climatic variability and change. Using a temporal analogue approach, this study examines 30 years of climate data (1989–2019), along with operational (grooming hours) and performance (permit sales) indicators, to provide insight into the vulnerability and adaptive capacity of the Ontario snowmobile industry in a medium (RCP4.5) and high (RCP8.5) mid-century (2046–2060) emission scenario. The results underscore important temporal and spatial variability across Ontario’s 16 snowmobile districts, indicating that snowmobilers are highly resilient to marginal conditions, changing districts and switching from seasonal to daily permits in response to warming temperatures. The findings from this study can inform risk assessments in other major snowmobile markets (e.g., Canada, Europe, USA), with future research needs discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".