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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 Β z > 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 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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.168
Threshold uncertainty score0.789

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.030
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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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