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Record W2148085165

How new technology developments will lower wind energy costs

2009· article· en· W2148085165 on OpenAlexaff
Doug Hoffman, Tom Molinski

Bibliographic record

Venue2009 CIGRE/IEEE PES Joint Symposium Integration of Wide-Scale Renewable Resources Into the Power Delivery System · 2009
Typearticle
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsManitoba Hydro
Fundersnot available
KeywordsWind powerTurbineRenewable energyMarine engineeringTowerEnvironmental scienceMeteorologyScale (ratio)Wind hybrid power systemsOffshore wind powerNameplate capacityElectricity generationEngineeringPower (physics)Intermittent energy sourceElectrical engineeringCivil engineeringAerospace engineeringDistributed generationGeographyPhysicsCartography
DOInot available

Abstract

fetched live from OpenAlex

Wind Power is a clean and renewable energy technology that is generated, most commonly through the use of a three-bladed wind turbine mounted on a tall tower (80 m - 100 m). Although wind power is most commonly generated by larger, utility scale wind turbines (1.5 MW – 3 MW in size), smaller capacity (≪ 500 kW) wind turbines have also been used in commercial and community projects, but at a cost of almost three times higher than utility scale projects.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.242
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.007
GPT teacher head0.189
Teacher spread0.182 · 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 designBench or experimental
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

Citations17
Published2009
Admission routes1
Has abstractyes

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