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Record W2284287812 · doi:10.1680/maen.14.00017

Projected impact of climate change on waves at Mumbai High

2015· article· en· W2284287812 on OpenAlexaboutno aff
Satyavathi Pentapati, M. C. Deo, Jyoti Kerkar, P. Vethamony

Bibliographic record

VenueProceedings of the Institution of Civil Engineers - Maritime Engineering · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTropical and Extratropical Cyclones Research
Canadian institutionsnot available
FundersU.S. Department of Energy
KeywordsClimate changeSubmarine pipelineClimatologyEnvironmental scienceGeneral Circulation ModelGlobal warmingWave heightSignificant wave heightClimate modelReturn periodMeteorologyGeographyWind waveGeologyOceanography

Abstract

fetched live from OpenAlex

Structures built in the sea are traditionally designed according to historical climate observations or hindcasts. In geographical locations typical of India, such designs do not take the effect of future climate change into account. For structural safety, considerations of such effects are highly desirable. Many recent studies have demonstrated likely changes in the extreme wave conditions at different offshore locations. This paper reports similar results in the Mumbai High region, where the majority of India's offshore oil platforms are located. Using historical wind data provided by the National Centre for Environmental Prediction/National Centre for Atmospheric Research and a Canadian general circulation model for future data (CMIP5-RCP8.5), a numerical wave model of the past and future wave conditions was simulated over a 40-year period. A statistical analysis of both past and projected datasets obtained significant wave heights with a 100-year return. The comparison of wave heights derived from past and future datasets showed that wave magnitudes would increase at almost all locations within the Mumbai High area. The northern locations may undergo a significant rise from 10% to 28%, while the southern sites may experience a smaller increase, from almost zero to 10%, under the highest projected pathway of global warming.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.215
Threshold uncertainty score0.468

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)0.0000.000

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 head0.021
GPT teacher head0.225
Teacher spread0.204 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations4
Published2015
Admission routes1
Has abstractyes

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