Trends and multi-decadal variability of annual maximum precipitation for Seoul, South Korea
Bibliographic record
Abstract
Flood risk management is an important and difficult problem for the densely populated and rapidly urbanised city of Seoul, South Korea. This study characterises long-term trends and variability in the city's annual maximum daily precipitation (AMP) over multiple decades. Smoothing the time series reveals that recent decades have witnessed a steep upward trend in AMP. Continuous wavelet analysis shows that the AMP series has statistically significant power in the 32–60-year periodicity band between 1880 and 1960 (one full cycle is clearly visible in the smoothed series). This feature has an even wider scope in the annual total precipitation series, suggesting that a real oscillation exists. Four climate indices were investigated as possible explanatory variables for the AMP series using cross-wavelet analysis, but no significant coherence between the signals was found. Finally, mean AMP forecasts based on three interpretations of the past linear trend are provided for flood risk management.
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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 | 0.000 |
| 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".