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Record W4402626947 · doi:10.1109/mcs.2024.3432342

Floating Offshore Wind Farm Control via Turbine Repositioning: Unlocking the Potential Unique to Floating Offshore Wind

2024· article· en· W4402626947 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

VenueIEEE Control Systems · 2024
Typearticle
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsOffshore wind powerMarine engineeringTurbineSubmarine pipelineWind powerEnvironmental scienceControl (management)EngineeringAutomotive engineeringComputer scienceMechanical engineeringElectrical engineeringGeotechnical engineering

Abstract

fetched live from OpenAlex

Driven by the urgent need to displace fossil fuels for carbon emission reduction and climate change mitigation, offshore wind energy has emerged as a highly promising renewable energy source. By harnessing the power of strong and consistent wind across the vast expanse of the open sea, offshore wind turbines offer extraordinary potential for generating enormous amounts of electricity, supplying abundant clean energy to coastal regions and beyond. Situated at sea, offshore turbines can capitalize on stronger and steadier wind, resulting in higher energy production compared to their onshore counterparts. Furthermore, offshore wind turbines offer the additional advantage of minimizing visual impacts and noise pollution on human beings, making them particularly suitable for densely populated areas near coastlines.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.591
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.007
GPT teacher head0.217
Teacher spread0.209 · 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