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Record W2944891991 · doi:10.1029/2018ja026245

A Comparison of Cross‐Track Ion Drift Measured by the Swarm Satellites and Plasma Convection Velocity Measured by SuperDARN

2019· article· en· W2944891991 on OpenAlexaff
A. V. Koustov, Daniel Lavoie, Alexei Kouznetsov, J. K. Burchill, D. J. Knudsen, Robyn Fiori

Bibliographic record

VenueJournal of Geophysical Research Space Physics · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsNatural Resources CanadaUniversity of CalgaryUniversity of Saskatchewan
Fundersnot available
KeywordsSwarm behaviourConvectionGeophysicsGeologyGeodesyInterplanetary magnetic fieldLatitudeSatellitePhysicsPlasmaMeteorologySolar windComputer scienceAstronomy

Abstract

fetched live from OpenAlex

Abstract Cross‐track ion drifts measured by the Swarm A satellite are compared with colocated line‐of‐sight Super Dual Auroral Radar Network (SuperDARN) velocities in approximately the same directions. More than 200 Swarm A passes over four polar cap SuperDARN radars in the Northern and Southern Hemispheres are considered. Overall, the Swarm‐based velocities are larger than the SuperDARN velocities; the slope of the best fit line to the data is ~0.67. Somewhat stronger differences are found when Swarm A measurements for the entire year 2016 are compared with SuperDARN vector data from global‐scale convection maps. Swarm ion drift data demonstrate known features of the high‐latitude convection patterns, for example, reverse convection cells at interplanetary magnetic field Βz > 0. The latitudes of the convection reversal boundary inferred from SuperDARN are found to be in reasonable agreement with those determined from Swarm A and Swarm B, with Swarm‐based latitudes occurring roughly 1° more equatorward, typically.

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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.030
GPT teacher head0.335
Teacher spread0.306 · 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

Citations28
Published2019
Admission routes1
Has abstractyes

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