A probability model of three potential precursors during tornado occurrences: the Italian case.
Bibliographic record
Abstract
57 tornadoes with intensity Enhanced Fujita Scale 2 or larger that occurred in Italy in the period 2000–2018 are analysed in order to investigate the way two meteorological parameters, namely Wind Shear, calculated in 0-1 km and 0-6 km layers, and CAPE, affect their development. For this purpose, a statistical analysis, by means of homogeneity tests, conditional probabilities and a multivariate analysis via copulas is performed, using two different re-analysis datasets (ERA-Interim and ERA-5). The study indicates that: (a) tornadoes occur mostly in correspondence with positive anomalies of both variables; (b) probability of occurrence is correlated with WS, and (c) is maximum when either WS or CAPE are large. Also, the probability does not increase significantly with CAPE, although sufficiently large values are needed for tornado occurrence. These results are similar for both re-analyses we used and suggest that the selected parameters are reliable precursors for Italian tornadoes.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".