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Record W2799578532 · doi:10.4043/28711-ms

Offshore Wind Sediment Stability Evaluation Framework

2018· article· en· W2799578532 on OpenAlexaff
Craig Jones, Sam McWilliams, Georg Engelmann, Aimee Thurlow, Jesse Roberts

Bibliographic record

VenueOffshore Technology Conference · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsAlberta Energy
FundersOffice of Energy Efficiency and Renewable EnergyU.S. Department of the InteriorSandia National LaboratoriesU.S. Department of Energy
KeywordsSeabedSediment transportSubmarine pipelineSedimentGeologySeafloor spreadingOffshore wind powerShoreCurrent (fluid)SubseaMarine engineeringOceanographyGeomorphologyEngineeringWind power

Abstract

fetched live from OpenAlex

Abstract Developing sound methods to evaluate risk of seabed mobility and alteration of sediment transport patterns in the near-shore coastal regions due to the presence of Offshore Wind (OW) infrastructure is critical to project planning, permitting, and operations. OW systems may include seafloor foundations, cabling, floating structures with gravity anchors, or a combination of several of these systems. Installation of these structures may affect the integrity of the sediment bed, thus affecting seabed dynamics and stability. It is therefore necessary to evaluate hydrodynamics and seabed dynamics and the effects of OW subsea foundations and cables on sediment transport. A methodology is presented here to map a site's sediment (seabed) stability and can in turn support the evaluation of the potential for these processes to affect OW deployments and the local ecology. Sediment stability risk maps are developed for a site offshore of Central Oregon. A combination of geophysical site characterization, metocean analysis, and numerical modeling is used to develop a quantitative assessment of local scour and overall seabed stability. The findings generally show the presence of structures reduces the sediment transport in the lee area of the array by altering current and wave fields. The results illustrate how the overall regional patterns of currents and waves influence local scour near pilings and cables.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.613
Threshold uncertainty score0.999

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 studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0380.001

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 head0.026
GPT teacher head0.272
Teacher spread0.245 · 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; both teacher heads agree on what is shown here.

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

Citations3
Published2018
Admission routes1
Has abstractyes

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