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Record W2966981138 · doi:10.1109/jsen.2019.2935616

Phase Discrimination in Marine Icing Using a Coplanar Capacitive Array

2019· article· en· W2966981138 on OpenAlexafffund
Abdulrazak Elzaidi, Vlastimil Masek, Yuri S. Muzychka

Bibliographic record

VenueIEEE Sensors Journal · 2019
Typearticle
Languageen
FieldEngineering
TopicIcing and De-icing Technologies
Canadian institutionsMemorial University of Newfoundland
FundersMitacsEquinorMinistry of Education, Libya
KeywordsIcingCapacitive sensingDielectricElectronic engineeringPhase (matter)Computer scienceEnvironmental scienceEngineeringElectrical engineeringMeteorologyPhysics

Abstract

fetched live from OpenAlex

This paper describes the development of an array of coplanar capacitive sensors applied to marine icing. Current atmospheric icing monitoring systems consider single phase conditions in their operation. Marine icing conditions present a unique environment where the liquid water phase effects cannot be neglected and require a novel approach. We have conducted an initial proof of concept and propose a new icing monitoring system which can distinguish between the individual phases. A numerical model confirmed our initial hypothesis of the system’s ability to discriminate the multiphase domains based on the array of geometrically dissimilar capacitive sensors. In addition, we also developed a novel experimental technique based on a comparative study under constant conditions to eliminate the need for an independent ice accretion monitoring system normally required in sensor development. The new approach promises a better characteristic in marine icing monitoring systems or in similar applications where multiphase dielectric is present.

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 score0.272
Threshold uncertainty score0.533

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 integrity0.0000.001
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 head0.020
GPT teacher head0.259
Teacher spread0.239 · 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

Citations7
Published2019
Admission routes2
Has abstractyes

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