A Global Assessment of Non-Stationarity in Extreme Precipitation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<p>Rapid urban development, along with human modifications in river discharge (both frequency and magnitude) increase the need to design safe and resilient infrastructure. In addition, continental-domain studies show that there are significant changes in the intensity and frequency of the extreme rainfall events. Importantly, Earth System Models predict that these changes will continue to grow in the future. Consequently, flood frequency from heavy precipitation events is expected to increase, thereby threatening human society and the environment. Therefore, the stationary climate assumption — the idea that the future variability of the system will remain within the limits observed in the past record — may not be valid and should be carefully examined. Despite the existing awareness of potential non-stationarity, there has been a limited research on analysis of non-stationary of extreme precipitation at the global scale. This motivated us to conduct a comprehensive global study to compare the performance of non-stationary and stationary models in describing precipitation extremes.</p>
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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 | 0.000 | 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) | 0.000 | 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