MétaCan
Menu
Back to cohort
Record W2110972059 · doi:10.1029/2006ja011725

Bounce‐averaged diffusion coefficients for field‐aligned chorus waves

2006· article· en· W2110972059 on OpenAlexaff
Yuri Shprits, R. B. Horne, Danny Summers

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2006
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsPlasmaspherePhysicsComputational physicsVan Allen radiation beltMagnetosphereScatteringDiffusionPitch angleRay tracing (physics)GeophysicsPlasmaOptics

Abstract

fetched live from OpenAlex

Whistler mode chorus emissions in the Earth's magnetosphere extend from the plasmapause to the boundary of trapping. Knowledge of the pitch angle and energy scattering rates is essential for accurate radiation belt modeling. Scattering rates, which are used as an input for radiation belt codes, should be evaluated dynamically and should account for the changes in plasma density, latitudinal and MLT distribution of waves, and wave spectral properties. Bounce‐averaged diffusion coefficients computed with the assumption of parallel wave propagation are compared to the results of the PADIE diffusion code, which takes into account the oblique propagation of waves and higher‐order resonances. The inaccuracies associated with the neglect of higher‐order resonances are compared to potential errors introduced by inaccuracies in determining plasma density and the latitudinal distribution of waves. Numerical sensitivity tests show that the errors associated with the neglect of the high‐order scattering are smaller than inaccuracies associated with the uncertainties in the parameters of the codes.

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)0.005
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 score0.027
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.013
GPT teacher head0.298
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

Citations135
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Geophysical Research AtmospheresSame topicIonosphere and magnetosphere dynamicsFrench-language works237,207