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Record W1879898335 · doi:10.1002/2015gl065245

Observations of coincident EMIC wave activity and duskside energetic electron precipitation on 18–19 January 2013

2015· article· en· W1879898335 on OpenAlexfundno aff
Lauren Blum, Alexa Halford, R. M. Millan, J. W. Bonnell, J. Goldstein, Maria Usanova, M. J. Engebretson, M. Ohnsted, G. D. Reeves, H. J. Singer, M. Clilverd, X. Li

Bibliographic record

VenueGeophysical Research Letters · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsnot available
FundersCanadian Space AgencyNatural Environment Research CouncilSight Research UKDartmouth CollegeNational Aeronautics and Space Administration
KeywordsElectron precipitationPhysicsVan Allen radiation beltSubstormGeophysicsMagnetosphereAstrophysicsVan Allen ProbesElectronEmic and eticPrecipitationComputational physicsAtmospheric sciencesMeteorologyMagnetic fieldNuclear physics

Abstract

fetched live from OpenAlex

Abstract Electromagnetic ion cyclotron (EMIC) waves have been suggested to be a cause of radiation belt electron loss to the atmosphere. Here simultaneous, magnetically conjugate measurements are presented of EMIC wave activity, measured at geosynchronous orbit and on the ground, and energetic electron precipitation, seen by the Balloon Array for Radiation belt Relativistic Electron Losses (BARREL) campaign, on two consecutive days in January 2013. Multiple bursts of precipitation were observed on the duskside of the magnetosphere at the end of 18 January and again late on 19 January, concurrent with particle injections, substorm activity, and enhanced magnetospheric convection. The structure, timing, and spatial extent of the waves are compared to those of the precipitation during both days to determine when and where EMIC waves cause radiation belt electron precipitation. The conjugate measurements presented here provide observational support of the theoretical picture of duskside interaction of EMIC waves and MeV electrons leading to radiation belt loss.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.747
Threshold uncertainty score0.732

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 studies0.0000.000
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 head0.054
GPT teacher head0.313
Teacher spread0.260 · 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 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

Citations138
Published2015
Admission routes1
Has abstractyes

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