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Record W4297541766 · doi:10.5194/angeo-40-571-2022

Mid-latitude neutral wind responses to sub-auroral polarization streams

2022· article· en· W4297541766 on OpenAlexafffund
Daniel Billett, K. A. McWilliams, Robert Kerr, J. J. Makela, Alex T. Chartier, J. M. Ruohoniemi, Sudha Kapali, Mike A. Migliozzi, J. Riccobono

Bibliographic record

VenueAnnales Geophysicae · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaEuropean Space Agency
KeywordsGeologyIncoherent scatterPressure gradientAirglowGeophysicsThermosphereConvectionSolar windIonosphereDragAtmospheric sciencesPhysicsPlasmaMeteorologyMechanics

Abstract

fetched live from OpenAlex

Abstract. We investigate the response of the mid-latitude thermospheric neutral winds to a sub-auroral polarization stream (SAPS) event. Using red line (F region) airglow data from two Fabry–Pérot interferometers (FPIs), and F-region ionospheric flow velocities from four Super Dual Auroral Radar Network (SuperDARN) radars, the drivers behind changes seen in the neutral winds are explored within the context of the larger SAPS structure. Different, although strong, neutral wind responses to the SAPS are seen at the two FPI sites, even though they are relatively close geographically. We attribute the wind differences to the varying balance of pressure gradient, ion drag, and Coriolis forces, which ultimately depend on proximity to the SAPS. At the FPI site equatorward of the SAPS, pressure gradient and Coriolis forces drive the winds equatorward and then westward. At the FPI site co-located with the SAPS, the ion drag is strong and results in the winds surging westward before turning eastward when becoming influenced by dawnside sunward plasma convection drifts.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.327
Threshold uncertainty score0.929

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)0.0010.000

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 head0.008
GPT teacher head0.229
Teacher spread0.221 · 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 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

Citations5
Published2022
Admission routes2
Has abstractyes

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