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Record W4293187642 · doi:10.1177/0309524x221080469

Wind energy resources assessment of Cuba using the regional climate model PRECIS in high resolution scenarios of climate change RCPs

2022· article· en· W4293187642 on OpenAlexaff
Yoandy Alonso, Arnoldo Bezanilla‐Morlot, Milena Alpizar, Yosvany Martinez

Bibliographic record

VenueWind Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsRepresentative Concentration PathwaysWind speedClimate changeEnvironmental scienceClimatologyWind powerDownscalingClimate modelMeteorologyAnticycloneGeneral Circulation ModelCurrent (fluid)Horizontal resolutionClimate simulationGeographyOceanographyGeologyEngineering

Abstract

fetched live from OpenAlex

An analysis of the behavior of the wind speed using the regional climate model PRECIS in high resolution scenarios of climate change RCPs is presented. The projections indicate that throughout the century, the speed of the surface wind will continue to increase to a greater or lesser extent (depending on the scenario) in most of the national territory, mainly towards the coast north, as an intensification and westward shift of the anticyclone North Atlantic could occur. The most important thing about this increase is that allows to consolidate the current projection of the Cuban wind program, in which the construction of 13 wind farms is proposed, precisely where the wind potential of Cuba will be increased. Finally this increase is added to the wind speed outputs of the Numerical Wind Atlas of Cuba to estimate the values of wind speed over the future periods.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.444
Threshold uncertainty score0.884

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.029
GPT teacher head0.242
Teacher spread0.214 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2022
Admission routes1
Has abstractyes

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