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Record W2016615196 · doi:10.1119/1.3517032

Characterization of a Piezoelectric Buzzer Using a Michelson Interferometer

2010· article· en· W2016615196 on OpenAlexaff
S. J. Lloyd, Mark Paetkau

Bibliographic record

VenueThe Physics Teacher · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsThompson Rivers University
Fundersnot available
KeywordsPiezoelectricityMichelson interferometerInterferometryBuzzerOpticsMaterials scienceMicroscopeAstronomical interferometerCharacterization (materials science)PhysicsOptoelectronicsAcoustics

Abstract

fetched live from OpenAlex

A piezoelectric material generates an electric potential across its surface when subjected to mechanical stress;1 conversely, the inverse piezoelectric effect describes the expansion or contraction of the material when subjected to some applied voltage. Piezoelectric materials are used in devices such as doorbell buzzers, barbeque igniters, and also as the scanning and approach mechanisms in scanning probing microscopy. The assembly of a scanning tunnelling microscope (STM)2 at Thompson Rivers University has motivated a characterization of the sensitivity and hysteresis3 of piezoelectric discs using a Michelson interferometer. The investigation uses an interferometer4 and a simple photodiode circuit to track the fringes. As a possible undergraduate lab, the measurement provides an introduction to piezoelectric materials (including hysteresis), the Michelson interferometer, and data acquisition techniques.

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)0.001
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: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.013
GPT teacher head0.274
Teacher spread0.261 · 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

Citations2
Published2010
Admission routes1
Has abstractyes

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