Estimating extreme sea levels combining systematic observed skew surges and historic record sea levels
Bibliographic record
Abstract
The estimation of the sea levels with a high return period is crucial for coastal planning and the assessment of coastal flooding risk. Coastal facilities are designed to very low probabilities of failure and hence the design values are affected by significant uncertainties. Some recent coastal floods due to exceptional surges suggest that the design performed with the current statistical approaches may sometimes be significantly underestimated. This presentation is a contribution to the use of historical observations to improve the estimation of extreme sea levels. Historic records consist in observed major sea level values. The corresponding skew surges may be estimated but the exhaustiveness of historical skew surges, which is an essential criterion for an unbiased statistical inference cannot be guaranteed. . Indeed, Extreme skew surges can easily remain unnoticed if they occur at low or moderate high tide and do not generate extreme sea levels. This study proposes to combine, in a single Bayesian inference procedure, series of measured skew surges for the recent period and extreme sea levels for the historic period. The method is tested on four sites (tide gauges) located on the French Atlantic and Channel coasts. The proposed method appears to provide unbiased quantile estimates and to be more reliable than previously proposed approaches to include historic records in coastal sea level or surge statistical analyses.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".