A Decision Support Tool for Assessing Climate Change Impacts on Extreme Rainfall Processes
Bibliographic record
Abstract
This study proposes a decision support tool (SDEXRAIN) for assessing the climate change impacts on extreme rainfall processes at a given location. More specifically, the SDEXRAIN consists of two components: (i) a spatial statistical downscaling model to describe the linkage between global climate variables and daily annual maximum rainfalls at a given site; and (ii) a temporal statistical downscaling model to describe the relations between daily and sub-daily annual maximum rainfalls. Results of a numerical application of this tool using NCEP re-analysis and observed precipitation data available at two locations with completely different climatic conditions (Seoul station in South Korea and Dorval station in Canada) have indicated that the proposed SDEXRAIN could accurately describe the relations between global climate predictors and daily and sub-daily annual maximum rainfalls at a given site. Hence, this tool can be used for assessing the climate change impacts on extreme rainfalls at a location of interest.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 teacher head, 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".