Arena ice quality and perspectives on optimizing performance and addressing emerging challenges
Bibliographic record
Abstract
Maintaining optimal ice surfaces in arenas is essential for ensuring athlete performance and safety in sports such as hockey, figure skating, and curling. This study combines expert survey responses from 55 North American ice arena managers with existing literature to identify best practices for managing ice conditions. Key factors, including ice temperature, humidity, thickness, and water quality were examined to identify areas needing empirical validation. While expert opinions offer valuable insights, controlled experiments are necessary to determine how compressive strength, friction, and Total Dissolved Solids (TDS) influence ice performance. Lower ice temperatures improve compressive strength and durability for hockey, while slightly warmer temperatures offer better grip for figure skating. Maintaining humidity between 40% and 50% aligns with industry guidelines, balancing friction while limiting frost formation and sublimation. Water quality plays a critical role, yet conflicting recommendations highlight the need for further research to determine optimal TDS levels. Additionally, emerging contaminants such as microplastics and PFAS pose environmental concerns that warrant monitoring. Future research should bridge the gap between expert knowledge and scientific evidence to refine best practices and promote sustainable ice arena operations.
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 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 | 0.001 | 0.001 |
| 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".