ASSESSMENT OF TORNADO HAZARD MAPS FOR SOUTHERN ONTARIO
Bibliographic record
Abstract
Probabilistic quantitative tornado hazard assessment is often based on the consideration that the spatial distribution of tornado occurrence is homogeneous in a region. While this assumption simplifies the analysis, it could over- and under- estimate tornado hazard for regions with lower and higher tomadic activity if an average rate of tornado occurrence is employed. The degree of over- and under-estimation is unknown. This study is focused on the assessment of the impact of spatial inhomogeneity of tornado occurrence on the estimated tornado hazard, and the development of tornado hazard maps for southern Ontario. The obtained results indicate that the tornado hazard at the factoted design wind speed level is much smaller than the wind hazard due to synoptic winds even if the spatial inhomogeneity of tornado occurrence is considered. Furthermore, the results show that the spatial inhomogeneity of tornado occurrence has significant impact on the spatial tornado hazard level, that the return period values of tornado wind speed vary significantly over the considered region, and that the inhomogeneity must be considered in developing probabilistic quantitative tornado hazard maps. Also, an attempt is made to assemble an approach for assessing the tornado hazard considering the uncertainty in the tornado occurrence rate in time and space. The quantification of this uncertainty is carried out by using the hierarchical Bayesian modeling and Markov Chain Monte Carlo technique. Results showed that it is feasible to use such an assembled approach to assess the tornado hazard maps, which incorporate the uncertainty in tornado occurrences.
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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 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".