COMBINED TEMPERATURE-PRECIPITATION MODES AND THEIR RELATIONSHIP WITH LARGE-SCALE CLIMATE INDICES IN PARANÁ, SOUTHERN BRAZIL (1980-2014)
Bibliographic record
Abstract
In recent decades the Northeast of Brazil experienced several episodes of intense droughts while other regions were affected by heavy rainfall events that caused severe flooding. The variability of temperature and precipitation in Brazil are associated with large-scale climatic indices, such as the El Niño Southern Oscillation (ENSO), the Multidecadal Atlantic Oscillation (AMO) and the Tropical North Atlantic (TNA). In this study, quantiles 25 and 75 of temperature and precipitation were used to determine the climatic trends in terms of number of days for the different modes (warm and dry, warm and humid, cold and dry or cold and wet). Subsequently, correlation analyzes were carried out with nine different climatic indices that influence the regional climate of southern Brazil. Our results highlighted the absence of a dominant mode throughout the seasons and over the years. We also found spatio-temporal trends in this region. In addition, except for the warm-dry mode where 8 out of 10 stations were correlated with the Niño1 + 2 index, there were few correlations between the modes and the different climate indices used in this research. Despite the increasing temperature trends, and a complex and heterogeneous variations in precipitation regime, our results did not indicate any significant changes in the modes nor their relationship with the climate indices.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".