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Record W4385332488 · doi:10.1088/2631-8695/acebb9

Sensors for the measurement of shear stress and shear strain-a review on materials, fabrication, devices, and applications

2023· article· en· W4385332488 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEngineering Research Express · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsShear (geology)Capacitive sensingMaterials sciencePiezoresistive effectShear stressFiber Bragg gratingFabricationStructural health monitoringPiezoelectricityComposite materialMechanical engineeringEngineeringElectrical engineeringOptoelectronics

Abstract

fetched live from OpenAlex

Abstract Shear sensors are used for measuring shear stress and shear strain in solid bodies when mechanical forces are applied. For the preparation of these sensors, researchers reported innovative materials either alone or in the form of blends, alloys, and composites. Shear sensors are not easily available for purchase, therefore, this review focuses on the working principles of various kinds of shear sensors being explored by researchers. Several technologies and materials are used, such as piezoelectric materials, piezoresistive materials, Fiber Bragg Grating, capacitive sensing, and structural colors. This article also looks at fabrication-based challenges that restrict the commercial use of shear sensors. A variety of shear sensor devices are evaluated for measuring shear stress/strain for many different applications such as health monitoring and biomedical, robotics, and or fracture in materials.

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.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.501
Threshold uncertainty score0.474

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.058
GPT teacher head0.317
Teacher spread0.259 · 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