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Record W2179794189 · doi:10.1002/2015ja021781

Electron trapping and acceleration by kinetic Alfven waves in the inner magnetosphere

2015· article· en· W2179794189 on OpenAlexafffund
Anton Artemyev, R. Rankin, Manuel Blanco

Bibliographic record

VenueJournal of Geophysical Research Space Physics · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Space Agency
KeywordsPhysicsGyroradiusMagnetosphereElectronComputational physicsAtomic physicsKinetic energyParticle accelerationAccelerationTrappingPitch angleWavelengthAmplitudeMagnetic fieldClassical mechanicsGeophysicsOpticsQuantum mechanics

Abstract

fetched live from OpenAlex

Abstract In this paper we study the interaction of kinetic Alfven waves generated near the equatorial plane of the magnetosphere with electrons having initial energies up to ∼100 eV. Wave‐particle interactions are investigated using a theoretical model of trapping into an effective potential generated by the wave parallel electric field and the mirror force acting along geomagnetic field lines. It is demonstrated that waves with an effective potential amplitude on the order of ∼100–400 V and with perpendicular wavelengths on the order of the ion gyroradius can trap and efficiently accelerate electrons up to energies of several keV. Trapping acceleration corresponds to conservation of the electron magnetic moment and, thus, results in a significant decrease of the electron equatorial pitch angle with time. Analytical and numerical estimates of the maximum energy and probability of trapping are presented, and the application of the proposed model is discussed.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.316
Teacher spread0.285 · 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 designSimulation or modeling
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

Citations66
Published2015
Admission routes2
Has abstractyes

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