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Record W2047776185 · doi:10.1063/1.3606450

Suprathermal plasma analyzer for the measurement of low-energy electron distribution in the ionosphere

2011· article· en· W2047776185 on OpenAlexaff
Manabu Shimoyama, K.‐I. Oyama, Takumi Abe, A. W. Yau

Bibliographic record

VenueReview of Scientific Instruments · 2011
Typearticle
Languageen
FieldEngineering
TopicPlasma Diagnostics and Applications
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPlasmaLangmuir probeSounding rocketElectronAtomic physicsPhysicsPlasma diagnosticsSpectrum analyzerElectron temperatureRange (aeronautics)Electron densityIonosphereComputational physicsMaterials scienceOpticsNuclear physicsGeophysics

Abstract

fetched live from OpenAlex

It is commonly believed that an energy transfer from thermal to suprathermal electrons (<several eV) greatly affects the energy budget of the ionosphere. However, observation of electron energy spectrum in this energy range is quite limited because of technical difficulties of measurement. We have developed an instrument to measure electron energy distribution from thermal to suprathermal energy continuously with high-energy resolution of about 0.15 eV. The measurement principle is based on the combination of a retarding potential analyzer with a channel electron multiplier (CEM) and the Druyvesteyn method, which derives energy distribution from the current-voltage characteristics. The capability of detecting plasma space potential enables absolute calibration of electron energy. The instrument with a small vacuum pump, which is required for the CEM to work in low-vacuum region, was first successfully tested by a sounding rocket S-310-37 in the ionospheric E region. The instrument is expected to provide new opportunities to measure energy distribution of thermal and non-thermal electrons in low-density plasma, where a Langmuir probe cannot measure electron temperature because of low plasma density.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.751
Threshold uncertainty score0.186

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 head0.023
GPT teacher head0.226
Teacher spread0.203 · 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 designOther design
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

Citations4
Published2011
Admission routes1
Has abstractyes

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