Analysis of extreme weather events over Satara district of western Maharashtra using RClimDex model
Bibliographic record
Abstract
The study on Extreme temperature and precipitation based climatic indices was carried out at Department of Agricultural Meteorology, College of Agriculture, Pune entitled “Analysis of extreme weather events of Western Maharashtra using RclimDex model” during 2021-2023. Daily resolution data required for the monitoring, identification, and attributing to changes in the climatic extreme conditions for Satara district was obtained from website of India Meteorological Department, Pune. The extreme temperature and precipitation-based indices were calculated by using RClimDex model. The RClimDex model was developed and maintained by Xuebin Zhang and Yang Feng at the Climate Research Branch of the Meteorological Service of Canada. The Mann-Kendall trend test was used in this study to determine whether there was a statistically significant increasing or decreasing trend of indices. If significant trend was observed the Sen’s slope estimator test was employed to detect whether the trend is positive or negative. The data analyzed (40 years data) showed variations in frequency and intensity of extreme weather events. Out of the total 19 temperature-based extreme indices for Satara district, 6 indices showed a decreasing trend, 11 indices showed an increasing trend, and two indices did not occur in Satara region. In terms of the precipitation based indices over Satara district out of nine precipitation indices 7 indices showed significantly positive (increasing) trend whereas only two indices namely annual maximum consecutive 1-day precipitation (RX1day) and consecutive dry days (CDD) where maximum number of consecutive days with RR
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".