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Record W2418192641 · doi:10.1186/s40623-016-0472-7

Calibration and assessment of Swarm ion drift measurements using a comparison with a statistical convection model

2016· article· en· W2418192641 on OpenAlexafffund
Robyn Fiori, A. V. Koustov, D. H. Boteler, D. J. Knudsen, J. K. Burchill

Bibliographic record

VenueEarth Planets and Space · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of CalgaryUniversity of SaskatchewanNatural Resources Canada
FundersNatural Resources CanadaNatural Sciences and Engineering Research Council of CanadaCanadian Space AgencyEuropean Space Agency
KeywordsSwarm behaviourConvectionRadarF regionInterplanetary magnetic fieldDrift velocityPhysicsIonosphereSolar windGeodesyComputational physicsGeophysicsIonRemote sensingMeteorologyGeologyPlasmaComputer science

Abstract

fetched live from OpenAlex

The electric field instruments onboard the Swarm satellites make high-resolution measurements of the F-region ion drift. This paper presents an initial investigation of preliminary ion drift data made available by the European Space Agency. Based on data taken during polar cap crossings, we identify large offsets in both the along-track and cross-track components of the measured ion drift. These offsets are removed by zeroing drift values at the low-latitude boundary of the high-latitude convection pattern. This correction is shown to significantly improve agreement between the Swarm ion drift measurements and velocity inferred from a radar-based statistical convection model for periods of quasi-stability in the solar wind and interplanetary magnetic field. Agreement is most pronounced in the cross-track direction (R = 0.60); it improves slightly (R = 0.63) if data are limited to periods with IMF B z < 0. The corrected Swarm data were shown to properly identify the convection reversal boundary for periods of IMF B z < 0, in full agreement with previous radar and satellite measurements, making Swarm ion drift measurements a valuable input for ionospheric modeling.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.950
Threshold uncertainty score0.218

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 head0.023
GPT teacher head0.268
Teacher spread0.244 · 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 designSimulation or modeling
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

Citations17
Published2016
Admission routes2
Has abstractyes

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