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Record W2171823540 · doi:10.1109/imtc.2005.1604370

Design and Manufacture of Surface Acoustic Wave Sensors for Real-Time Weigh-in-Motion

2005· article· en· W2171823540 on OpenAlexaff
Brian Mc Cormack, D. Geraghty, Margaret O’Mahony

Bibliographic record

Venue2005 IEEE Instrumentationand Measurement Technology Conference Proceedings · 2005
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Resonator Technologies
Canadian institutionsTrinity College
FundersScience Foundation Ireland
KeywordsFlexibility (engineering)Surface acoustic waveStrain gaugeElectronic engineeringTransducerWaferEngineeringComputer scienceProcess (computing)AcousticsElectrical engineering

Abstract

fetched live from OpenAlex

This paper presents the design and manufacture of Surface Acoustic Wave (SAW) strain sensors which can be produced in a university environment. These sensors can be interrogated wirelessly and operated without a power supply, which allows for greater measurement flexibility than conventional strain gauge systems. The design of an optimised sensor requires accurate experimental data on a number of process parameters and configurations, which for this project requires the testing of several prototype designs. The large prototyping costs typical of SAW devices can be significantly reduced if the design and manufacturing processes are integrated. This paper outlines such a system, where device function and processing issues are integrated at each stage. The result is a range of sensors which have been "designed for manufacture", optimising both the variety of devices per wafer and the yield.

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: none
Teacher disagreement score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.028
GPT teacher head0.226
Teacher spread0.198 · 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

Citations6
Published2005
Admission routes1
Has abstractyes

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