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Record W4293796867 · doi:10.3389/fphy.2022.977286

Dipolarization-induced energetic electron precipitation during continuously active periods and isolated substorms

2022· article· en· W4293796867 on OpenAlexfundno aff
M. A. Shukhtina, В. А. Сергеев, D. D. Rogov

Bibliographic record

VenueFrontiers in Physics · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsnot available
FundersRussian Science FoundationCanadian Space AgencyUniversity of Calgary
KeywordsElectron precipitationPrecipitationSuperposition principleAtmospheric sciencesIonosphereAbsorption (acoustics)StormElectronPhysicsComputational physicsEnvironmental scienceGeophysicsMeteorologyMagnetospherePlasmaOpticsNuclear physics

Abstract

fetched live from OpenAlex

Recently we proposed a spatio-temporal model of auroral absorption for isolated substorms based on linear prediction filter technique, which describes the precipitation effects as a sum of properly weighted and time-delayed contributions of short dipolarizations/injections inferred from magnetic MPB index. Here we apply a similar approach to a more general and practically important type of continuous activity including substorms clusters, which is accompanied by intense energetic electron precipitation lasting for many hours and may affect ozone concentration and climate. Unexpectedly, in spite of very different geophysical background, the derived precipitation response to unit-scale injection appeared almost identical to that obtained for isolated substorms. Significant part of absorption variance during active non-storm periods turns out to be the result of superposition of previous injections with short memory less than 4 h. Our results indicate that, while the injection efficiency is roughly the same, large difference in precipitated fluxes and ionospheric response between two different types of activity is mostly provided by a more frequent appearance and increased intensities of dipolarizations/injections during active periods. In both event types dipolarizations are the decisive factor which determines energetic electron precipitation.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.366
Threshold uncertainty score0.811

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 head0.003
GPT teacher head0.189
Teacher spread0.187 · 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 designObservational
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

Citations2
Published2022
Admission routes1
Has abstractyes

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