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Record W2268272936 · doi:10.5194/angeo-34-227-2016

Seasonal and solar cycle variations in the ionospheric convection reversal boundary location inferred from monthly SuperDARN data sets

2016· article· en· W2268272936 on OpenAlexafffund
A. V. Koustov, Robyn Fiori

Bibliographic record

VenueAnnales Geophysicae · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsNatural Resources CanadaUniversity of Saskatchewan
FundersNatural Resources CanadaNatural Sciences and Engineering Research Council of CanadaCanadian Space Agency
KeywordsInterplanetary magnetic fieldLatitudeIonosphereAtmospheric sciencesConvectionDuskHigh latitudeSolar cycleGeologyClimatologyEnvironmental scienceGeophysicsGeodesySolar windPhysicsMeteorologyPlasmaAstronomy

Abstract

fetched live from OpenAlex

Abstract. Multi-year (1995–2013) velocity data collected by the Super Dual Auroral Network (SuperDARN) HF radars are considered to investigate seasonal and solar cycle variations of the convection reversal boundary (CRB) location for interplanetary magnetic field (IMF) Bz < 0. By considering monthly data sets we show that the CRB is at higher latitudes in summer between 1995 and 2007. The poleward shifts are on the order of 2–5°. After 2007, the seasonal effect weakens, and the highest latitudes for the CRB start to occur during the winter time. We show that the CRB latitudes decrease with an increase of the IMF transverse component at a rate of (1–2°)/2 nT. Because of this effect, on average, the CRB latitudes are lower during high solar activity periods with stronger IMFs. We also confirm the effect of the CRB dawn-dusk shifts related to the IMF changes in the IMF By sign.

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 score0.740
Threshold uncertainty score0.476

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 communication0.0000.001
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 head0.011
GPT teacher head0.232
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

Citations12
Published2016
Admission routes2
Has abstractyes

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