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Record W25994713 · doi:10.1121/1.4919346

Experimental Investigations of Generalized Predictive Control for Tiltrotor Stability Augmentation

2001· article· en· W25994713 on OpenAlexaboutno aff
W Nixon Mark, W Langston Chester, D Singleton Jeffrey, J Piatak David, G Kvaternik Raymond, L Bennett Richard, K Brown Ross

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2001
Typearticle
Languageen
FieldEngineering
TopicAeroelasticity and Vibration Control
Canadian institutionsnot available
Fundersnot available
KeywordsModel predictive controlStability (learning theory)EngineeringWind tunnelControl theory (sociology)AeronauticsControl (management)Computer scienceAerospace engineeringArtificial intelligenceMachine learning

Abstract

fetched live from OpenAlex

This is an acoustic and articulatory study of Canadian French rhotic vowels, i.e., mid front rounded vowels /ø œ̃ œ/ produced with a rhotic perceptual quality, much like English [ɚ] or [ɹ], leading heureux, commun, and docteur to sound like [ɚʁɚ], [kɔmɚ̃], and [dɔktaɹʁ]. Ultrasound, video, and acoustic data from 23 Canadian French speakers are analyzed using several measures of mid-sagittal tongue contours, showing that the low F3 of rhotic vowels is achieved using bunched and retroflex tongue postures and that the articulatory-acoustic mapping of F1 and F2 are rearranged in systems with rhotic vowels. A subset of speakers' French vowels are compared with their English [ɹ]/[ɚ], revealing that the French vowels are consistently less extreme in low F3 and its articulatory correlates, even for the most rhotic speakers. Polar coordinates are proposed as a replacement for Cartesian coordinates in calculating smoothing spline comparisons of mid-sagittal tongue shapes, because they enable comparisons to be roughly perpendicular to the tongue surface, which is critical for comparisons involving tongue root position but appropriate for all comparisons involving mid-sagittal tongue contours.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.857
Threshold uncertainty score0.200

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 head0.251
Teacher spread0.234 · 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 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

Citations4
Published2001
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicAeroelasticity and Vibration ControlFrench-language works237,207