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Record W2807905625 · doi:10.1177/0309524x18780400

A post-processing module based on Cressman’s analysis to improve the Wind Energy Simulation Toolkit mapping system

2018· article· en· W2807905625 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

VenueWind Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsWind speedMeteorologyWind powerWind resource assessmentEnvironmental scienceAtlas (anatomy)Data processingWind directionComputer scienceRemote sensingGeographyEngineeringGeologyDatabase

Abstract

fetched live from OpenAlex

In this work, a post-processing module based on Cressman’s method of objective analysis is added to the Wind Energy Simulation Toolkit in order to improve the accuracy of the numerical wind atlas of Cuba. Mean wind speed surface observations at 35 meteorological stations and mean wind speed observations at 10, 30, 50, and 100 m height above ground level collected at a network of 58 observation towers are assimilated in the Cressman analysis. Furthermore, the 3-year numerical wind atlas generated for the same period of time is considered as the first guess for the Cressman method. A new wind atlas of Cuba is generated and verified using observation records at 32 meteorological stations and 10 observation towers distributed over the country. In addition, the capability of the new post-processing scheme to adding information on the temporal variability of the wind resource is explored.

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 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: none
Teacher disagreement score0.693
Threshold uncertainty score0.943

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.205
Teacher spread0.197 · 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