Extreme skew surge estimation combining systematic skew surges and historical record sea levels on the English Channel and North Sea coasts
Bibliographic record
Abstract
Abstract Coastal planning implies the estimation of extreme sea levels. As the distribution of astronomical high tides can be predicted, most recent publications suggest focusing on the estimation of extreme skew surges. Historical information, record sea levels observed before the beginning of systematic tide gauge recordings, can improve estimations. The corresponding skew surges can be estimated but are not necessarily exhaustive. Indeed, some historical extreme skew surges can remain unnoticed if they are combined with low or moderate tides, or for a variety of reasons. To deal with this exhaustiveness issue, a previous publication proposed an unbiased method for combining systematic period skew surges with historical period extreme sea levels. This method appeared more reliable than previously proposed approaches. The present study aims at presenting a broader evaluation of this method, based on its application to nine sites located on the English Channel and North Sea coasts. The method is also improved to consider several historical periods and various types of historical information. Results confirm the method to be reliable, useful, and relevant. A number of recommendations is also formulated for the selection and use of historical information for sea level frequency 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| 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".