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Record W4280514199 · doi:10.1038/s41598-022-12189-2

Destructive coastal sea level oscillations generated by Typhoon Maysak in the Sea of Japan in September 2020

2022· article· en· W4280514199 on OpenAlexaff
Igor Medvedev, Alexander B. Rabinovich, Jadranka Šepić

Bibliographic record

VenueScientific Reports · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTropical and Extratropical Cyclones Research
Canadian institutionsFisheries and Oceans Canada
FundersRussian Science FoundationDivision of Ocean SciencesEuropean Research CouncilHrvatska Zaklada za Znanost
KeywordsTyphoonStorm surgeSeicheClimatologyStormTropical cycloneOceanographySea levelForcing (mathematics)Cyclone (programming language)PeninsulaEnvironmental scienceSubsidenceSea breezeGeologyMeteorologyGeography

Abstract

fetched live from OpenAlex

Typhoon Maysak (Julian in the Philippines) was a powerful tropical cyclone that strongly impacted coastal regions of the Sea of Japan on 2-4 September 2020. Destructive winds, violent storm waves, and intense rainfall occurred in Japan, on the Korean Peninsula, and in Far-Eastern Russia. Devastating coastal floods caused severe damage to coastal infrastructure and to ships and boats anchored in harbours and were responsible for numerous deaths. Our study indicates that the main reason for the destructive floods was the superposition of storm surge, extreme seiches (meteorological tsunamis), and surf beats. At various sites, different types of sea level oscillations prevailed depending on the atmospheric forcing, local topographic properties, and resonant shelf/coastal zone features. The principal forcing factors of these oscillations were atmospheric pressure and wind stress, but the exact generation mechanism of each specific type of oscillation was strongly site dependent. The uniqueness of the sea level response at each site is the main challenge in our understanding of the generation process and to the mitigation of the hazardous consequences of possible future events.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.027
GPT teacher head0.246
Teacher spread0.219 · 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.

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

Citations39
Published2022
Admission routes1
Has abstractyes

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