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Record W4281830499 · doi:10.37775/eis.2022.1.3

Jeges szárnyalak aerodinamikájának vizsgálata

2022· article· hu· W4281830499 on OpenAlexaff
Balázs Csőre, László Kollár, Dániel Fenyvesi

Bibliographic record

VenueMérnöki és Informatikai Megoldások · 2022
Typearticle
Languagehu
FieldSocial Sciences
TopicHungarian Social, Economic and Educational Studies
Canadian institutionsSavaria (Canada)
Fundersnot available
KeywordsNAKHumanitiesPhysicsArtEngineeringTelecommunications

Abstract

fetched live from OpenAlex

A kutatás témája szélturbina lapátmetszetek jegesedésének hatása az aerodinamikai tényezokre. Alapátokon felhalmozódott jég akár 50%-os teljesítményvesztéshez is vezethet széleromuvek esetén. Avizsgálat tárgya a NACA négyjegyu szárnyprofil sorozat néhány tagja. A tanulmányban a jegesedéstaz ónos szitálásra jellemzo körülmények okozzák, és a szimulációk kimutatják a lapát geometriájánakés az állásszög változtatásának hatását a kialakult jég alakjára, valamint a jég alakjának hatását azaerodinamikai tényezokre. A kapott eredményekbol következtetni lehet arra, hogy az egyes lapátgeometriákesetén milyen mértékben romlik a lapát aerodinamikája jegesedés hatására, illetve, hogy alapátprofilt érdemes-e olyan területen használni, ahol a jegesedéssel számolni kell.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.777
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0080.001
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0230.004

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 head0.024
GPT teacher head0.277
Teacher spread0.253 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations1
Published2022
Admission routes1
Has abstractyes

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