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Record W2032824944 · doi:10.1002/eej.1136

Characteristic performance of radio‐frequency (RF) plasma heating using inverter RF power supplies

2002· article· en· W2032824944 on OpenAlexfundno aff
Takahiro Imai, Hiroyuki Sawada, Norifumi Hattori, Yoshihiko Uesugi, Shuichi Takamura

Bibliographic record

VenueElectrical Engineering in Japan · 2002
Typearticle
Languageen
FieldEngineering
TopicPlasma Diagnostics and Applications
Canadian institutionsnot available
FundersInstitute of Circulatory and Respiratory Health
KeywordsRF power amplifierRadio frequencyPlasmaPower (physics)Antenna (radio)Materials scienceDielectric heatingVoltageElectrical engineeringNuclear magnetic resonancePhysicsOptoelectronicsEngineeringAmplifierNuclear physics

Abstract

fetched live from OpenAlex

Abstract High‐heat‐flux plasmas are produced by high‐power (∼14 kW) ICRF heating using inverter power supplies in the linear diverter simulator NAGDIS‐II. The power flow of radiated RF power is investigated by a calorimetric method. From conventional power calculation using antenna voltage and current, about 70% of the RF power is radiated into the plasma. But the increase of the plasma heat load along the magnetic field by RF heating is about 10% of the RF power. Through this experiment, we find that about half of the RF power is lost at the antenna surface through the formation of RF‐induced sheath, and about 30% of the power is lost into the wall of the vacuum vessel through the charge exchange and elastic collision of ions with neutrals. © 2002 Scripta Technica, Electr Eng Jpn, 138(4): 34–41, 2002; DOI 10.1002/eej.1136

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0030.001

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 head0.009
GPT teacher head0.183
Teacher spread0.174 · 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 source (direct Gemma or distilled Codex), 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
Published2002
Admission routes1
Has abstractyes

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