Projections of declining outdoor skating availability in Montreal due to global warming
Bibliographic record
Abstract
Abstract Outdoor skating is a valued and culturally important winter activity in Canada that is vulnerable to warming winter temperatures resulting from anthropogenic climate change. Changes to the outdoor skating season (OSS) due to climate change have been estimated from historical weather records using the occurrence of daily temperatures below a particular temperature threshold as a proxy for rink availability. However, research on the actual weather conditions needed for outdoor rinks to be maintained in reasonable condition is limited. In this study, we used historical weather data and daily reports on outdoor rinks in Montréal to identify which daily or multi-day temperature variable can best act as an indicator of outdoor ice rink availability. We evaluated a series of temperature variables using a logistic regression to predict the likelihood of open rinks during each day of the season. Using AIC scores to select the best model, we found that the mean of the preceding six-day maximum temperature was the best predictor of skating availability. Using this temperature predictor, we then projected changes in the duration of the future OSS in Montréal based on global climate model data, downscaled to the island of Montréal using the MarkSim Weather Generator. Our results showed that the mean OSS duration in Montréal would range from a 15% to a >75% decline by 2090 depending on which future emissions scenario we follow. In a scenario that limits global temperature rise to below 2.0 °C (RCP 2.6), we projected a 41 day mean OSS duration at the end of this century. By contrast, under a business-as-usual emissions pathway (RCP 8.5), the average length of the OSS in Montréal could decline to only 11 days per year. Our results suggest that very ambitious climate change mitigation will be required to preserve outdoor skating in Montréal in the face of ongoing global climate change.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.003 | 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".