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Record W7132511933

Bell 412 system identification: comparing methods and tools

2019· article· en· W7132511933 on OpenAlexvenueaboutno aff
Susanne Seher-Weiß, Steffen Greiser, Johannes Wartmann, Arthur W. Gubbels, Joseph Ricciardi, Kenneth Hui

Bibliographic record

VenueNPARC · 2019
Typearticle
Languageen
FieldEngineering
TopicAerospace and Aviation Technology
Canadian institutionsnot available
Fundersnot available
KeywordsIdentification (biology)System identificationPoint (geometry)Flight testData modelingKey (lock)
DOInot available

Abstract

fetched live from OpenAlex

Over the last decades system identification has become a standard method for developing rotorcraft models from flight test data. Using data from the Bell 412 Advanced Systems Research Aircraft (ASRA) helicopter operated by the National Research Council of Canada, different identification methods and tools are applied to a common database. The identified 6-DoF rigid-body models at one flight condition are compared in detail regarding eigenvalues and the match in both time and frequency domain. The development of a global model from point models that were identified at the different flight conditions is described. Finally, several possible extensions of the rigid-body models are investigated assessing corresponding improvement in model fidelity.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.410
Threshold uncertainty score0.267

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 head0.017
GPT teacher head0.263
Teacher spread0.246 · 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 designBench or experimental
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 routes2
Has abstractyes

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