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Record W2328985282 · doi:10.1115/imece2010-40425

Investigation of Chaotic Dynamics in MST Based Precision Sensors

2010· article· en· W2328985282 on OpenAlexaff
Mehdi Tabe Arjmand, S. Śpiewak

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicInertial Sensor and Navigation
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsChaoticComputer scienceInertial frame of referenceComputationMicrosystemDynamics (music)Control theory (sociology)Phase spaceStatistical physicsAlgorithmPhysicsClassical mechanicsArtificial intelligenceAcoustics

Abstract

fetched live from OpenAlex

This paper is concerned with the accuracy and signal detection limits in ultra precision, Microsystems Technologies based inertial sensors. It focuses on conditions that lead to chaotic dynamics, detection of such dynamics. Development of the needed constitutive-empirical, scale-able models is presented and illustrated by example of axially preloaded microflexures. To eliminate uncertainties caused by the accumulation of numerical errors in commonly used engineering software we employ arbitrary precision computations. We detect chaotic dynamics in the investigated systems by means of Poincare map technique to show the evolution of phase space from deterministic to chaotic behavior.1

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.660
Threshold uncertainty score0.212

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 head0.007
GPT teacher head0.198
Teacher spread0.191 · 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
Published2010
Admission routes1
Has abstractyes

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