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Record W1866236101 · doi:10.1002/2014ja019790

Polar summer mesospheric extreme horizontal drift speeds during interplanetary corotating interaction regions (CIRs) and high‐speed solar wind streams: Coupling between the solar wind and the mesosphere

2014· article· en· W1866236101 on OpenAlexaff
Young‐Sook Lee, S. Kirkwood, Young‐Sil Kwak, Kyung‐Chan Kim, G. G. Shepherd

Bibliographic record

VenueJournal of Geophysical Research Space Physics · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsYork University
FundersAir Force Research LaboratoryKorea Astronomy and Space Science Institute
KeywordsAtmospheric sciencesSolar windPhysicsMagnetosphereWind speedInterplanetary spaceflightPolarElectric fieldInterplanetary magnetic fieldEnvironmental scienceMeteorologyPlasmaAstronomy

Abstract

fetched live from OpenAlex

Abstract We report the observation of echo extreme horizontal drift speed (EEHS, ≥ 300 m s −1 ) during polar mesospheric (80–90 km) summer echoes (PMSEs) by the VHF (52 MHz) radar at Esrange, Sweden, in years of 2006 and 2008. The EEHS occur in PMSEs as correlated with high‐speed solar wind streams (HSSs), observed at least once in 12–17% of all hours of observation for the two summers. The EEHS rate peaks occur either during high solar wind speed in the early part of the PMSE season or during the arrival of interplanetary corotating interaction regions (CIRs) followed by peaks in PMSE occurrence rate after 1–4 days, in the latter part of the 2006 summer. The cause of EEHS rate peaks is likely under the competition between the interval of the CIR and HSS passage over the magnetosphere. A candidate process in producing EEHS is suggested to be localized strong electric field, which is caused by solar wind energy transfer from the interaction of CIR and HSS with the magnetosphere in a sequential manner. We suggest that EEHS are created by strong electric field, estimated as > 10–30 V m −1 at 85 km altitude, exceeding the mesospheric breakdown threshold field.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.405
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.002
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 head0.022
GPT teacher head0.280
Teacher spread0.258 · 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.

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

Citations14
Published2014
Admission routes1
Has abstractyes

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