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Record W1544517653

Nonlinear material properties of quartz determined by the resonator method

2014· article· en· W1544517653 on OpenAlexaff
C.K. Hruska

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2014
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Resonator Technologies
Canadian institutionsYork University
Fundersnot available
KeywordsElectrostrictionNonlinear systemDielectricResonatorMaterials scienceQuality (philosophy)QuartzMathematical analysisPiezoelectricityMathematicsPhysicsComposite materialOptoelectronicsQuantum mechanics
DOInot available

Abstract

fetched live from OpenAlex

Attempts to provide least-squares estimates of all 31 independent thrid-order nonlinear electromechanical constants of alpha quartz using the resonator method produce parameters containing third-order elastic constants linearly combined with the remaining three types of electromechanical nonlinearities: electroelastic, electrostrictive and dielectric. To maximize the yield of the resonator method in terms of these latter three types of nonlinear constants, the third-order elastic constants must be "imported" from an external source. They can be introduced, with the same effect, before or after the least-squares process is executed. Their reliability is crucial for the quality of the electroelastic constants and apparently (considering the size of their respective standard errors) less important for the electrostrictive and third-order dielectric constants. A comparison with the results of others, yielding in most cases an excellent agreement, is facilitated by introducing uniformity into the values of the linear constants used for their determination. This produces changes in the calculated nonlinear parameters but does not remove the few differences which were noted.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.136
GPT teacher head0.471
Teacher spread0.335 · 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 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
Published2014
Admission routes1
Has abstractyes

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