Impact of Environmental Factors on Energy Balance and Ice Growth in Winter Recreational Waterways: A Study of the Rideau Canal Skateway
Bibliographic record
Abstract
The impact of climate change on the Rideau Canal Skateway (RCS), an outdoor skating rink, has become increasingly evident in recent years. This research focuses on growing high-quality ice for skating on the RCS using an energy balance method that integrates field data and numerical simulations. The aim is to provide insights that support decision-making and help develop strategies to extend the RCS skating season. The findings highlight the importance of strategic interventions, considering the time sensitivity of actions in response to air temperature fluctuations, snowfall events, and rainfall events that affect ice growth. The research emphasizes the multifactor nature of ice growth, illustrating the interactions among various climatic variables. A coupled heat transfer model was used to simulate changes in ice thickness, forced by environmental variables that were measured using devices installed at the weather station in the RCS. Results indicate that a thick layer of snow negatively impacts ice formation due to its insulating properties, which can reduce or stop ice growth and necessitate careful snow management. The results underscore the critical role of timely actions, such as surface snow clearing or intentional flooding, in mitigating the adverse effects of climate change. Overall, this research advances our understanding of the complex factors governing ice growth and stability along the RCS and offers practical insights for mitigating the impacts of climate change on the system.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".