A Statistical Investigation of Mesoscale Precursors of Significant Tornadoes: The Italian Case Study
Bibliographic record
Abstract
In this study, mesoscale environments associated with 57 significant tornadoes occurring over Italy in the period 2000–2018 are analyzed. The role of the vertical Wind Shear in the lower and middle troposphere, in terms of low-level shear (LLS) and deep-level shear (DLS), and of the convective available potential energy (CAPE) as possible precursors of significant tornadoes is statistically investigated. Wind shear and CAPE data are extracted from the ERA-5 and ERA-Interim reanalyses. Overall, the study indicates that: (a) values of these variables in the two uppermost quartiles of their statistical distribution significantly increases the probability of tornado occurrences; (b) the probability increases for increasing values of LLS and DLS, and (c) is maximum when either wind shear or CAPE are large. These conclusions hold for both the reanalysis datasets and do not depend upon the season and/or the considered area. With the possible exception of weak tornadoes, which are not included in our study, our results show that large wind shear, in the presence of medium-to-high values of CAPE, are reliable precursors of 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".