Events of Heavy Rainfall and Strong Winds in Sao Paulo State, Brazil
Bibliographic record
Abstract
The relationships of strong winds and heavy rainfall in Sao Paulo State, Brazil, was investigated. Wind data from Project Elektro/Climatico collected by thirteen Platforms of Data Collection were used. The results showed that in the west sector the occurrence of strong winds was higher in the center and south areas and decreased northwards. In the central sector the occurrence of strong wind episodes was higher southwards while in the litoral it was lower due to probably the lack of wind data collected during the austral summer. In Teodoro Sampaio, Andradina, Santa Rita d'Oeste, Votuporanga (west), Rio Claro, Tatui (central) and Ubatuba (litoral) 15, 18, 28, 12, 11, 7 and 3 extreme events occurred, respectively (maximum wind higher than 20 ms-1 together with rainfall higher than 25 mm day-1). A case study of an episode of extreme event which occurred in Andradina on 15 October 2009 was examined. Heavy rainfall accompanied with high winds and strong divergence at 200 hPa and ascending motion were observed in the region due to intense convection along a cold front that moved fast over Sao Paulo State. The identification of regions with strong winds and their relation with heavy precipitation must drive electrical energy generation and distribution what is important nowadays. So, the main importance of this research is its applicability to the energy sector.
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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 | 0.000 | 0.000 |
| 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".