Bibliographic record
Abstract
Severe hail activities have significant impacts on our society because they damage property and are dangerous to people and animals. However, we have little knowledge on recent changes in geographic locations of severe hail activity center over Canada. Prior to exploring this, we have carried out Canada hail data consistency and reliability checks using solid trend analyses of three independent methods for time series of hail counts and days, and robust verifications of reported hail data by a recently developed approach of sample generation by replacement. Here, we discover for the first time a statistically significant east shift of Canada severe hail activity and total hail activity using discriminant analysis . The spatial shift is from the western portion of continental Canada during 2005–2013 to the eastern Canada with a maritime environment during 2014–2022. With increase of hail severity, the hail activities increase from the colder period 2005–2013 to the warmer period 2014–2022. Our composite analyses show that over the continental Canada, the hail activities are enriched through thermodynamically driven convective instability and precipitable water associated with the warming climate, as well as dynamically driven processes such as vertical wind shear and vertically integrated water vapor flux convergence. Over the maritime Canada with the colder condition, the hail activities are enhanced by dynamically driven moisture advection and convergence as well as vertical wind shear, thermodynamically driven process of precipitable water, and partially due to convective instability. This research promotes our understanding of climate change impact on hail activities, shedding lights on long-term hail projection, adaptation, and mitigation strategies.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".