Extreme sea level oscillations in the Sea of Japan caused by typhoons Maysak and Haishen in September 2020
Bibliographic record
Abstract
Two hazardous typhoons, Maysak and Haishen, in September 2020 produced extreme sea level oscillations in the Sea of Japan. These typhoons generated three different types of sea level variations: 1) storm surges (with typical periods from several hours to 1.5 days), 2) extreme seiches (with periods from a few minutes to several tens of minutes), and 3) storm-generated infragravity waves (with periods up to 3-5 min). The data from eleven tide gauges on Russian, Korean, and Japanese coasts were used to examine the properties of these oscillations. The relative contribution of the three separate sea level components and their statistical characteristics (duration, wave heights, and periods) were estimated. The periods of the main eigen modes of individual bays and harbours in the Sea of Japan were estimated based on spectral analysis of longterm background records at the corresponding sites. The results of wavelet analysis show the frequency properties and the temporal evolution of individual sea level components. We found that high-frequency sea level oscillations at stations Preobrazheniye and Rudnaya Pristan have a “white noise” spectrum, caused by the dominance of infragravity waves. A high correlation was detected between the variance of high-frequency sea level oscillations at these stations and the significant wind wave height evaluated from ERA5 for this water area.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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 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".