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Record W2952213813 · doi:10.4271/2019-01-1991

CH148 Icing Flight Test Performance Data Correlation

2019· article· en· W2952213813 on OpenAlexaff
Daniel Griffiths

Bibliographic record

VenueSAE International Journal of Advances and Current Practices in Mobility · 2019
Typearticle
Languageen
FieldEngineering
TopicIcing and De-icing Technologies
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsIcingRotor (electric)Test dataCorrelationParagraphEngineeringMathematicsComputer scienceMeteorologyPhysicsMechanical engineeringGeometry

Abstract

fetched live from OpenAlex

<div class="section abstract"><div class="htmlview paragraph">This paper presents results of correlation of an empirical code to CH148 Cyclone<sup>®</sup> icing flight test performance data. Predicted icing main rotor torque penalties, generated using the Sikorsky Generalized Rotor Performance (GRP) model with the integral DELICE icing performance module, are compared to flight test results. DELICE methodology adjustments are shown that lead to CH148 test data correlation and improved correlation to previously-examined S-92A™ icing flight test performance data. Finally, a comparison of results between GRP and Sikorsky-Maryland FreeWake is shown, providing an indication of the influence of rotor wake methodology on icing model correlation.</div></div>

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.467
Threshold uncertainty score0.316

Codex and Gemma teacher scores by category

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 communication0.0000.002
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 head0.027
GPT teacher head0.335
Teacher spread0.307 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2019
Admission routes1
Has abstractyes

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