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Record W2088479589 · doi:10.1117/12.600728

Web-based actuator selection tool

2005· article· en· W2088479589 on OpenAlex
John D. W. Madden, Luca Filipozzi

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

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2005
Typearticle
Languageen
FieldEngineering
TopicDielectric materials and actuators
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsActuatorMaterials scienceElectroactive polymersComputer scienceInterface (matter)Artificial muscleProcess (computing)Mechanical engineeringComposite materialEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Device designers are continually confronted with the challenge of selecting the best actuator for a task and developers of new actuators are seeking applications for which their technologies are suitable. A web-based interface is presented that enables designers to input basic needs (force, displacement, frequency, cycle life, dimensions, voltage and power available) and retrieve an initial evaluation of the suitability of the various actuator technologies in the database. The prototype contains data for a number of emerging technologies including conducting polymers, dielectric elastomers, ferroelectric polymers, thermal and magnetic shape memory alloys, carbon nanotube actuators, liquid crystal elastomers, ionic polymer metal composites and mammalian skeletal muscle. The system is very early in the development process, and it is hoped that feedback from the EAP community will help guide the growth of and establish the need for this tool.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.278
Threshold uncertainty score1.000

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 communication0.0000.001
Open science0.0010.000
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 head0.204
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