Variabilidad climática de la precipitación en el estado de Ceará, Nordeste de Brasil
Bibliographic record
Abstract
The state of Ceará is a semi-arid region located in the Northeast region of Brazil, characterized by an irregular rainy season, great climate variability mainly driven by the El Niño–Southern Oscillation (ENSO), Sea Surface Temperature (SST) of the tropical South Atlantic and extreme weather events. Droughts and their effects were studied to determine their frequency and help reduce their economic, social and environmental impacts. For that, we evaluated the space-time variability of the Standardized Precipitation Index (SPI) and characterized the drought for the twelve hydrographic regions of the State of Ceará in the scales of 3, 6 and 12 months. The data comprise the period 1980-2020 considering the monthly values of precipitation provided by the Cearense Foundation of Meteorology and Water Resources (FUNCEME). During the years 1982 and 1993, the SPI detected the greatest droughts in the state. It was also verified that 1996 and 1998 were the years with less intense dry events, presented in the 3, 6 and 12 month scales of the SPI. The index proved to be a useful tool for identifying drought in the study area at different time scales. Using wavelet analysis we found increases in spectral power at periodicities of 4-10 years, especially around 1982 and 2011, but these oscillations do not seem to be significant above the red noise spectrum. We found that cooler and warmer ENSO conditions and tropical South Atlantic SST variability were related to wetter rainy seasons, while opposite SST conditions to drier seasons.
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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 | 0.002 |
| 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 | 0.000 |
| 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".