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Record W4281727822 · doi:10.1109/tim.2022.3214606

Digital Wire Analyzer of Mechanical Tension, Electrical Continuity, and Isolation

2022· article· en· W4281727822 on OpenAlexfundno aff
S. Prince, Pratyush Anand, James Battat, Russell Farnsworth, N. Felt, R. Guénette, S. Kubota, A. Li, E. Murdock, J. Oliver, C. Stanford, J.J.L. Weaver

Bibliographic record

VenueIEEE Transactions on Instrumentation and Measurement · 2022
Typearticle
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsnot available
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaOffice of ScienceSyracuse UniversityUniversity of Wisconsin-MadisonHigh Energy PhysicsU.S. Department of Energy
KeywordsSpectrum analyzerVoltageWire speedTension (geology)AcousticsVibrating wireElectrical engineeringNetwork analyzer (electrical)SIGNAL (programming language)Materials scienceFundamental frequencyResonance (particle physics)Measuring instrumentElectronic engineeringEngineeringPhysicsComputer scienceMechanical engineering

Abstract

fetched live from OpenAlex

A digital instrument that allows the measurement of the mechanical tension of an array of wires of known length and density, and the testing of their electrical continuity and isolation, has been developed. The instrument measures wire tension by measuring the fundamental frequency of the wire. Its working principle is to apply direct high voltages on neighbouring wires of a wire under test and sweeping the frequency of an alternating high voltage that is also applied on those neighbors. A resonance is observed in the readout signal of the middle wire when the frequency of the alternating high voltage coincides with its fundamental frequency. The instrument automates the process over 128 wires, with eight read out simultaneously. An accuracy of 1% in the measurement of tension is achieved by this digital wire analyzer.

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 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 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 score0.428
Threshold uncertainty score0.436

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 communication0.0000.000
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 head0.022
GPT teacher head0.231
Teacher spread0.209 · 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 teacher head, 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
Published2022
Admission routes1
Has abstractyes

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