A Canadian Hail Climatology Based on Elevation and the Hail-Thunderstorm Ratio
Bibliographic record
Abstract
This paper examines the relationship between elevation and the hail-thunderstorm ratio. Hail data from the Weatherlogics Hail Database (WHD) was used to calculate the average annual hail hours in urban areas. The hail-thunderstorm ratio was then calculated as the quotient of average annual hail hours and average annual thunderstorm hours. The hail-thunderstorm ratio was calculated for all hail (diameter >= 5 mm) and severe hail (diameter >= 20 mm). The relationship between elevation and the hail-thunderstorm ratio was then examined, with coefficients of determination (r2) of 0.78 and 0.65 found for all hail and severe hail, respectively. Using this relationship, the root-mean-square error between actual hail hours and predicted hail hours was found to be 0.6 and 0.2 hours, for all hail and severe hail, respectively. The relationships were then used to construct national hail climatologies for Canada using gridded elevation data and a thunderstorm hours climatology. A strong correlation of 0.98 was also found between annual hail hours and annual hail days, allowing for a “national hail days” climatology to be produced. A maximum in all hail hours and hail days was found along the Alberta foothills, with secondary maxima in the British Columbia interior and southern Prairies.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".