MétaCan
Menu
Back to cohort
Record W3217795776 · doi:10.1109/mper.2002.4312591

Accurate Voltage Measurement by the Quadrature Method

2002· article· en· W3217795776 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

VenueIEEE Power Engineering Review · 2002
Typearticle
Languageen
FieldEngineering
TopicMagneto-Optical Properties and Applications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsVoltage dividerVoltageCapacitive sensingHigh voltageElectronic engineeringElectrical engineeringDropout voltageTransformerComputer scienceEngineering

Abstract

fetched live from OpenAlex

This paper introduces the quadrature method for measuring voltage using one or more electric field sensors. To date, all high-voltage sensors, from conventional inductive transformers to modern optical voltage transducers, have one or more of the following traits in common: large size and weight, high-voltage electrodes in close proximity, expensive and potentially hazardous insulation, and capacitive voltage division. Combined with the use of small electro-optic field sensors, the quadrature method enables voltage sensor designs that are free of these traits and that are, therefore, particularly ideal for high-voltage applications. It also allows for a trade-off between the accuracy of the voltage measurement and the number of required electric field sensors. Numerical simulations demonstrate the effectiveness of this technique.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.728
Threshold uncertainty score0.763

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)0.0010.000

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 head0.026
GPT teacher head0.231
Teacher spread0.204 · 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