Use of the spatial extremogram to form a homogeneous region centered on a target site for the regional frequency analysis of extreme storm surges
Bibliographic record
Abstract
Nuclear power plants in France are designed for low probabilities of failure. Nevertheless, some exceptional surges, considered as outliers, are not properly addressed by statistical models. Regional information may be used to mitigate the paucity of data and the influence of outliers. A regional frequency analysis (RFA) model assumes a homogenous behavior of the variable of interest at a regional scale. The first stage in an RFA is the delineation of homogeneous regions. We propose herein a new approach to form a homogenous region centered on a target site and using the spatial extremal dependence between observations (the spatial extremogram) to measure the neighborhood between sites. Skew surge data set collated at a total of 19 sites located on the French coast (Atlantic and English Channel) were used as the case study for this contribution (with La Rochelle as a target site). Once a physically plausible region of interest is defined, a related issue regards the statistical homogeneity of the region of interest. The L-moment-based homogeneity tests of Hosking and Wallis, widely used in hydrology, are used in this paper. The principle of the extremogram allows us to form a physically and statistically homogenous group of sites centered on a target site. It also allows to overcome the problem of the so-called "border effect" and these are two key original points of the developed concept.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".