Return Periods of Extreme Events in the Changing Climate: LEYP Model
Bibliographic record
Abstract
A rapid pace of climate change is now becoming evident by a marked increase in the frequency and intensity of weather extremes, and this trend is expected to continue with an increase in global warming in the coming decades.The paper presents the linear extension of the Yule process (LEYP) as a general stochastic model of environmental hazards induced by non-stationary climate conditions.The LEYP is a more versatile model than the Poisson process, as it can incorporate dependence among events occurring over time.In the paper, explicit expressions have been derived for the return period, a traditional measure of reliability that is commonly used in the design of infrastructure systems.Unlike the stationary climate, the return period between extreme events would continue to decrease as climate change effects would become more pronounced in the future.Examples presented in the paper demonstrate that a modest degree of statistical dependence among events leads to a significant reduction in the return period, i.e., a remarkable increase in the frequency of extreme events.Therefore, existing design codes would need to be revised to accommodate such non-stationary changes to ensure a high level of safety of infrastructure systems in the changing climate.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".