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Record W3025204198 · doi:10.1002/9781119359203.ch17

Tropical Cyclone–Induced Storm Surges and Wind Waves in the Bay of Bengal

2020· other· en· W3025204198 on OpenAlexaff
Prasad K. Bhaskaran, A. D. Rao, T. S. Murty

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEarth and Planetary Sciences
TopicTropical and Extratropical Cyclones Research
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsStorm surgeBENGALBayTropical cycloneCyclone (programming language)Flooding (psychology)Coastal floodOceanographyStormFlood mythClimatologyGeologyEnvironmental scienceGeographyClimate changeSea level rise

Abstract

fetched live from OpenAlex

The Bay of Bengal and the Gulf of Mexico are the two water bodies on the globe that are most prone to storm surges generated by tropical cyclones. In this chapter, a review has been made of the storm surge problem in the Bay of Bengal region located in the North Indian Ocean. Using the contemporary numerical models, not only storm surge elevations but also coastal inundations were computed for some recent cyclones in the Bay of Bengal. These models included the interactions between storm surges, tides, and wind waves. However, it should be noted that one of the challenging issues, which still remains unsolved to a large extent, is computing the interaction between storm surges and river flooding, and the contribution of this interaction to coastal flooding and inundation. A dramatic example of such an interaction was during the 29 October 1999 cyclone on the Odisha coast.

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)0.000
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 score0.283
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0120.000

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 head0.242
Teacher spread0.215 · 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

Citations19
Published2020
Admission routes1
Has abstractyes

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