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Record W4294718912 · doi:10.3389/fspas.2022.978371

Spatial extent of the energetic electron precipitation region during substorms

2022· article· en· W4294718912 on OpenAlexfundno aff
Emma Bland, Tamás Bozóki, Noora Partamies

Bibliographic record

VenueFrontiers in Astronomy and Space Sciences · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsnot available
FundersNorges ForskningsrådCanadian Space AgencyUniversity of Calgary
KeywordsSubstormRiometerIonospherePhysicsGeophysicsNoise (video)Atmospheric sciencesFlux (metallurgy)Earth's magnetic fieldLatitudeGeomagnetic latitudeSatelliteElectron precipitationRadarComputational physicsMagnetosphereAstronomyMagnetic field

Abstract

fetched live from OpenAlex

The spatial coverage of the energetic electron precipitation (EEP) region during three substorms has been determined using a combination of ground-based instruments in North America. The primary datasets used to determine the presence or absence of EEP are riometer measurements of cosmic noise absorption (CNA); attenuation of the 10–11 MHz atmospheric noise in the D-region ionosphere determined using the Super Dual Auroral Radar Network (SuperDARN); amplitude variations in subionospheric very low frequency (VLF) propagation observed using the Antarctic-Arctic Radiation-belt (Dynamic) Deposition VLF Atmospheric Research Konsortium (AARDDVARK) VLF receivers, and total electron content measurements derived from global navigation satellite systems. Our results show that substorm EEP during moderate geomagnetic conditions can extend considerably equatorward or poleward of the average latitude limits reported in a previous statistical study of substorm EEP and an EEP model. This evidence is provided by the SuperDARN radars and the radar signature is not always accompanied by cosmic noise absorption measured by the riometers. Further work is required to determine which EEP energies and fluxes the SuperDARN radars are sensitive to, but we can conclude that EEP with sufficient flux to attenuate 10–11 MHz radiowaves by ∼5 dB reaches latitudes well equatorward of the statistical EEP latitude limits previously modelled for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m1"><mml:mo>&gt;</mml:mo></mml:math> 30 keV electrons.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.284

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 head0.004
GPT teacher head0.187
Teacher spread0.184 · 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

Citations6
Published2022
Admission routes1
Has abstractyes

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