Direct Joint Probability Method for Estimating Extreme Sea Levels
Bibliographic record
Abstract
A key design element in coastal structures is the crest elevation which protects against damages due to overflowing and overtopping. In order to avoid overflowing, the design crest elevation should be above the extreme flood level, which is usually composed of tides and storm surges but could also include tsunami, El Niño, and other climatologic and geologic effects. The extreme flood level may be determined with the annual maxima, simple addition, or joint probability methods (JPM). These methods have various limitations in terms of the amount of required data, the representation of factors contributing to sea level fluctuations, the ability to assess the joint probability of these factors, and the degree of data independence required. To minimize overtopping, the design crest elevation should be above the extreme sea level which is evaluated considering wave runup and the extreme flood level. Wave runup estimates are based on selected extreme flood levels and the extreme wave climate, data for which are often dependent. A modification of the JPM, the direct JPM (DJPM), is developed for estimating extreme flood and sea levels. This method may be applied to consider any number of dependent contributing factors. Data for the City of Richmond, B.C., Canada, are used to demonstrate the DJPM. The DJPM provides an estimate of the extreme flood level for Richmond that is within the same range as those obtained using traditional estimation methods. The results indicate a large difference between extreme flood and sea level estimates. The sea levels at Richmond are also increasing due to climate and tectonic effects. A hybrid direct joint probability-simple addition method is applied to consider these effects.
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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".