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Record W4393391783 · doi:10.1007/s13272-024-00731-z

New smart twisting active rotor (STAR): pretest predictions

2024· article· en· W4393391783 on OpenAlexfundno aff
Berend G. van der Wall, Joon W. Lim, Johannes Riemenschneider, Steffen Kalow, Gunther Wilke, D. Douglas Boyd, Joëlle Bailly, Yves Delrieux, Italo Cafarelli, Yasutada Tanabe, Hideaki Sugawara, Sung Nam Jung, Seong Hyun Hong, Do‐Hyung Kim, Hee Jung Kang, George N. Barakos, Rinaldo Steininger

Bibliographic record

VenueCEAS Aeronautical Journal · 2024
Typearticle
Languageen
FieldEngineering
TopicAerospace and Aviation Technology
Canadian institutionsnot available
FundersU.S. Army Combat Capabilities Development Command Aviation and Missile CenterEngineering and Physical Sciences Research CouncilKorea Aerospace Research InstituteNational Research Foundation of KoreaDefence Science and Technology LaboratoryMinistry of Science and ICT, South KoreaDeutsches Zentrum für Luft- und RaumfahrtKonkuk UniversityNational Research FoundationU.S. Army Combat Capabilities Development CommandU.S. Army Combat Capabilities Development Command Soldier CenterUniversity of GuelphNational Aeronautics and Space Administration
KeywordsAerospaceAeronauticsAerospace engineeringEngineeringAviationFlight testRotor (electric)Test (biology)MissileWind tunnelPayload (computing)Computer scienceMechanical engineeringComputer security

Abstract

fetched live from OpenAlex

Abstract A Mach-scaled model rotor with an active twist capability is in preparation for a wind tunnel test in the large low-speed facility of the German-Dutch wind tunnel (DNW) with international participation by the German Aerospace Center (DLR), US Army Combat Capabilities Development Command (DEVCOM) Aviation & Missile Center, National Aeronautics and Space Administration, French Aerospace Lab (ONERA), Korea Aerospace Research Institute, Konkuk University, Japan Aerospace Exploration Agency, Glasgow University, and DNW. To get the maximum benefit from the test and the most valuable data within the available test time, the tentative test matrix was covered by predictions of the partners, active twist benefits were evaluated, and support was provided to the test team to focus on the key operational conditions.

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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.009
GPT teacher head0.227
Teacher spread0.218 · 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

Citations9
Published2024
Admission routes1
Has abstractyes

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