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Record W4311691718 · doi:10.21203/rs.3.rs-2336305/v1

Light Ion Dynamics in the Topside Ionosphere and Plasmasphere during Geomagnetic Storms

2022· preprint· en· W4311691718 on OpenAlexafffund
I. P. Pakhotin, J. K. Burchill, M. Förster, Levan Lomidze

Bibliographic record

VenueResearch Square · 2022
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of Calgary
FundersInstitute of Geology and Geophysics, Chinese Academy of SciencesCanadian Space AgencyChinese Academy of SciencesU.S. Air ForceEuropean Space Agency
KeywordsPlasmasphereIonosphereGeomagnetic stormEarth's magnetic fieldStormGeophysicsAtmospheric sciencesPhysicsGeologyMeteorologyMagnetospherePlasmaMagnetic fieldNuclear physics

Abstract

fetched live from OpenAlex

Abstract Storm-time topside ionosphere plasma composition, especially the light ion fraction, is an important parameter which controls magnetosphere-ionosphere coupling, plays a part in the growth of local instabilities, and provides information about the ring current, ion upflow, movement of ionization and other important physical processes and parameters. Ion composition is difficult to estimate on fine scales as empirical models tend to be parametrized by fixed inputs, ignoring the role of memory in plasma, and to preferentially capture large scales, while ground radars have limited coverage. In particular, ionospheric composition measurements at mid-latitude are lacking. Here we show, using the new Swarm SLIDEM effective ion mass measurement, a superposed epoch analysis of storm-time dayside and nightside effective ion mass changes, demonstrating the extent and timescales of motion of the [O+]/[H+] transition height with the main phase of geomagnetic storms, as well as directly observing evidence for the latitude dependence of these dynamics.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

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 studies0.0000.000
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 head0.014
GPT teacher head0.295
Teacher spread0.280 · 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 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

Citations0
Published2022
Admission routes2
Has abstractyes

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