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Record W2808326490 · doi:10.1177/0309524x18780380

Transient atmospheric ice accretion on wind turbine blades

2018· article· en· W2808326490 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
TopicIcing and De-icing Technologies
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsIcingEnvironmental scienceAccretion (finance)Liquid water contentTransient (computer programming)TurbineMeteorologyTurbine bladeWind speedGeologyAtmospheric sciencesMaterials scienceEngineeringAerospace engineeringPhysicsCloud computingAstrophysics

Abstract

fetched live from OpenAlex

This article aims to predict ice loads on a wind turbine blade section at 80% of blade span, using FENSAP ICE. Using low and high liquid water content conditions of stratiform and cumuliform clouds, different icing events are simulated. Ice accretion predictions with single-shot and multi-shot approaches are presented. Blade surface roughness is also investigated, as well as the relationships between ice mass, liquid water content, median volume diameter, and temperature.

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: Empirical
Teacher disagreement score0.304
Threshold uncertainty score0.966

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.007
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