Probability of Tornado Occurrence across Canada
Bibliographic record
Abstract
The number of tornado observations in Canada is believed to be significantly lower than the actual occurrences. To account for this bias, the authors propose a Bayesian modeling approach founded upon the explicit consideration of the population sampling bias in tornado observations and the predictive relationship between cloud-to-ground (CG) lightning flash climatology and tornado occurrence. The latter variable was used as an indicator for quantifying convective storm activity, which is generally a precursor to tornado occurrence. The CG lightning data were generated from an 11-yr lightning climatology survey (1999–2009) from the Canadian Lightning Detection Network. The results suggest that the predictions of tornado occurrence in populated areas are fairly reliable with no profound underestimation bias. In sparsely populated areas, the analysis shows that the probability of tornado occurrence is significantly higher than what is represented in the 30-yr data record. Areas with low population density but high lightning flash density demonstrate the greatest discrepancy between predicted and observed tornado occurrence. A sensitivity analysis with various grid sizes was also conducted. It was found that the predictive statements supported by the model are fairly robust to the grid configuration, but the population density per grid cell is more representative to the actual population density at smaller resolution and therefore more accurately depicts the probability of tornado occurrence. Finally, a tornado probability map is calculated for Canada based on the frequency of tornado occurrence derived from the model and the estimated damage area of individual tornado events.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".