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Record W4318406659 · doi:10.1111/jfr3.12862

Extreme skew surge estimation combining systematic skew surges and historical record sea levels on the English Channel and North Sea coasts

2023· article· en· W4318406659 on OpenAlexaff
Laurie Saint Criq, Éric Gaumé, Yasser Hamdi, Taha B. M. J. Ouarda

Bibliographic record

VenueJournal of Flood Risk Management · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTropical and Extratropical Cyclones Research
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsSkewTide gaugeSurgeChannel (broadcasting)EstimationComputer scienceSea levelEnvironmental scienceOceanographyGeographyGeologyMeteorologyTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.341

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.061
GPT teacher head0.231
Teacher spread0.171 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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