Analysis of precipitation extremes in the Taihu Basin of China based on the regional L-moment method
Why this work is in the frame
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Bibliographic record
Abstract
China has suffered from increasingly severe flood events in recent years, most of which are caused by heavy rains. The substantial casualties and damage caused by flooding necessitates a better understanding of precipitation extremes, especially in heavily populated urban areas. Based on L-moments from a regional perspective, this paper analyzes precipitation extremes in the Taihu Basin, utilizing annual maximum daily precipitation and partial duration series at 96 rain gages. The comparison of regional and at-site analysis results shows that the former provides more robust estimates, especially in the upper tail of a distribution (higher quantiles). Also, the use of partial duration series, which captures more information about extreme events, was found to be preferable to describe the extreme precipitation events in the Taihu Basin. Given the recently observed more frequent occurrence and greater magnitude of precipitation extremes, it is suggested that the food design standard used in the basin should be updated, especially for the urbanizing zones.
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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 | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it