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Record W3036716349 · doi:10.3389/fspas.2020.00023

The Beam Plasma Interactions Experiment: An Active Experiment Using Pulsed Electron Beams

2020· article· en· W3036716349 on OpenAlexaff
G. D. Reeves, Gian Luca Delzanno, P. A. Fernandes, K. Yakymenko, John Lewellen, Michael Holloway, Dinh C. Nguyen, R. F. Pfaff, W. M. Farrell, D. E. Rowland, M. Samara, E. R. Sánchez, E. Spanswick, E. Donovan, V. Roytershteyn

Bibliographic record

VenueFrontiers in Astronomy and Space Sciences · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of Calgary
FundersNational Aeronautics and Space AdministrationU.S. Department of EnergyNational Science Foundation
KeywordsPhysicsSpacecraftWhistlerSounding rocketBeam (structure)Cathode rayPlasmaSpacecraft chargingElectronSpace physicsAtomic physicsComputational physicsAerospace engineeringNuclear physicsOpticsAstronomy

Abstract

fetched live from OpenAlex

The 1970s and 1980s were heydays for using active electron beam experiments to probe some of the fundamental physical processes that occur throughout the heliosphere and in astrophysical contexts. Electron beam experiments were used to study spacecraft charging and spacecraft-plasma coupling; beam-plasma interaction physics; magnetic bounce and drift physics; auroral physics; wave generation; and military applications. While these experiments were enormously successful, they were also limited by the technologies that were available at that time. New advances in space instrumentation, data collection, and accelerator technologies enable a revolutionary new generation of active experiments using electron beams in space. In this paper we discuss such an experiment, the Beam Plasma Interactions Experiment (Beam PIE), a sounding rocket experiment designed to (a) advance HEMT-based RF linear accelerator electron accelerator technology for space applications and (b) study the production of whistler and X-mode waves by modulated electron beams.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.714
Threshold uncertainty score0.585

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 studies0.0010.000
Scholarly communication0.0000.001
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 head0.011
GPT teacher head0.257
Teacher spread0.246 · 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

Citations24
Published2020
Admission routes1
Has abstractyes

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