Outdoor Ice Rinks in Ontario, Canada—An Oversimplified Model for Ice Water Equivalent and Operational Duration to Evaluate Changing Climate
Bibliographic record
Abstract
Outdoor ice rinks have long been a staple for inexpensive exercise and entertainment in cold environments. However, the possible deterioration of or impact on outdoor ice rinks from a changing climate is poorly understood due to no or little monitoring of data of such facilities. To investigate long-term changes in ice rinks over recent decades, an energy-balance-based ice rink model (with three versions considering precipitation and melt) was applied to a simulated ice rink for two representative area—Dorset of south-central Ontario and the Experimental Lakes Area (ELA) of northwestern Ontario, Canada. The model was calibrated and tested using four-year ice rink data (since limited data are available) and applied to a 40-year period starting in 1978 to reproduce the dates of rink-on and rink-off, rink duration in a season, and ice water equivalent under daily climate inputs, and to illustrate any changing trend in these variables, i.e., the ice rink responses to changed climate. Results showed no clear trend in any ice rink features over four decades, attributed to winter temperature that did not increase substantially (a weak driver), no change in events of rain-on-ice and snowfall-on-rink, and reduced wind speed (possibly slowing ice melting). This is the first trial of a physically based rink model to evaluate outdoor ice rinks. More in situ monitoring and in-depth modelling are necessary, and this model can help guide the monitoring.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".