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Record W4414158182 · doi:10.1016/j.asr.2025.09.010

Plasma flow velocity measured by Swarm and inferred from SuperDARN global-scale convection maps

2025· article· en· W4414158182 on OpenAlexafffund
A. V. Koustov, H. Fast, J. K. Burchill, Levan Lomidze, Alexei Kouznetsov, Mehdi Ghalamkarian Nejad

Bibliographic record

VenueAdvances in Space Research · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of CalgaryUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Space Agency
KeywordsConvectionSwarm behaviourPlasmaFlow (mathematics)Vertical velocity

Abstract

fetched live from OpenAlex

Measurements of the plasma flow velocity along track of Swarm satellite motion, the SLIDEM product, are compared with the plasma flow component inferred from SuperDARN global-scale map by considering 3 separate months of joint observations in 2014–2015. On the velocity-velocity scatter plots, the majority of points are found to be located away from the line of perfect agreement with larger Swarm-based velocities. For the case of quasi-laminar flows, the slopes of a linear fit line are ∼1.4. For the case of all measurements, the slopes are smaller or larger depending on the considered dataset. Obtained histogram distributions for the velocity ratio are centered at values in the range of 1.0–1.5, with typically smaller values than the slopes of the fit line. It is concluded that SLIDEM velocities are not dramatically different from SuperDARN-based velocities in the majority of cases and thus can be used for studies of various features in high-latitude plasma flows.

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)0.001
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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.010
GPT teacher head0.308
Teacher spread0.298 · 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
Published2025
Admission routes2
Has abstractyes

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