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Record W4396974115 · doi:10.23977/jeeem.2024.070118

Study on the sensitivity of electromagnetic wave to water content of oil-water mixture

2024· article· en· W4396974115 on OpenAlex
Wenxin Ye, Xinjian Wang

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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Electrotechnology Electrical Engineering and Management · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMagnetic and Electromagnetic Effects
Canadian institutionsnot available
Fundersnot available
KeywordsSensitivity (control systems)Water contentContent (measure theory)Environmental scienceGeologyEngineeringMathematicsGeotechnical engineeringElectronic engineering

Abstract

fetched live from OpenAlex

With the deepening of oilfield development and the increasing shortage of water resources, the accurate measurement of water content in oil-water mixture has become a key issue in oilfield development and production. This paper analyzes the sensitivity of electromagnetic waves to water content in water-oil mixture. By assuming that there is a linear relationship between the dielectric properties and water content, a water content detection model of crude oil is established. Full-wave simulation is made with COMSOL. The feasibility of water content detection with electromagnetic wave is studied by analyzing the receiving electric field and its frequency dependence. The research is instructive for non-destructive measurement of water content based on electromagnetic waves.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.459

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 head0.006
GPT teacher head0.197
Teacher spread0.191 · 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