Towards accurate ice accretion and galloping risk maps for Quebec: A data-driven approach
Bibliographic record
Abstract
Ice accretion poses a significant threat to infrastructure and public safety, particularly in regions prone to severe winter weather. Accurate ice accretion hazard mapping is essential for effective risk management and mitigation. While substantial progress has been made in mapping these hazards, most existing ice accretion maps rely on calculated ice accretion values rather than direct measurements, leading to potential inaccuracies. To address these limitations, this study leverages field measurement data from Hydro-Québec's glacimètre network to develop refined ice accretion maps for Quebec. The maximum annual ice accretion thicknesses are extracted, and a rigorous probability distribution fitting analysis is applied to generate 10-, 30-, and 50-year return period values. These values are interpolated using both inverse-distance weighted interpolation (IDWI) and kriging techniques, allowing for a comparative evaluation of interpolation methods. Additionally, galloping risks are assessed using the Performance-Based Ice Engineering (PBIE) framework, producing galloping risk maps for various return periods. By incorporating real-world data and comparing interpolation approaches, this research enhances the accuracy of ice accretion and galloping risk maps, providing more reliable hazard assessments for Quebec's infrastructure. • Development of refined ice accretion maps using field measurements. • Comparison of IDWI and kriging for ice accretion interpolation. • Assessment of galloping risk using the PBIE framework. • Identification of significant spatial variability in ice and galloping risk
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".