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Record W3203691743 · doi:10.2749/ghent.2021.0241

Hurricane risk assessment of offshore wind turbines under changing climate

2021· article· en· W3203691743 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueReport · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTropical and Extratropical Cyclones Research
Canadian institutionsUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsEnvironmental scienceOffshore wind powerWind powerSubmarine pipelineWind speedAtlantic hurricaneMeteorologyClimate changeHazardVulnerability (computing)ClimatologyEstimationRisk assessmentTropical cycloneOceanographyComputer scienceEngineeringGeographyGeology

Abstract

fetched live from OpenAlex

<p>Offshore wind energy is attracting increasing attention across the North America. However, the offshore wind turbines along the East Coast are extremely vulnerable to hurricane-induced hazards. The vulnerability to hurricanes is expected to change due to global warming’s effects. This study quantifies the risk of floating wind turbines (FWTs) subjected to hurricane hazards under current and future climate scenarios. The hurricane hazard estimation is achieved using a hurricane track model which generates a large synthetic database of hurricanes allowing for accurate risk estimation. The structural response of the FWTs during each hurricane event is obtained using an efficient physics-based 3-D model. The case study results involving a parked FWT indicate that the change in hurricane-induced risk, evaluated in terms of the magnification factor, to the FWTs would significantly increase with the intensity measure.</p>

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.

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 score0.012
Threshold uncertainty score0.998

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)0.0030.000

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 head0.021
GPT teacher head0.293
Teacher spread0.273 · 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