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Record W2800208568 · doi:10.1029/2017ja024974

Combined Contribution of Solar Illumination, Solar Activity, and Convection to Ion Upflow Above the Polar Cap

2018· article· en· W2800208568 on OpenAlexaff
Yu‐Zhang Ma, Qinghe Zhang, Zan‐Yang Xing, P. T. Jayachandran, J. Moen, R. A. Heelis, Yong Wang

Bibliographic record

VenueJournal of Geophysical Research Space Physics · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of New Brunswick
FundersNational Oceanic and Atmospheric AdministrationNatural Science Foundation of Shandong ProvinceNorges ForskningsrådNational Natural Science Foundation of China
KeywordsConvectionAtmospheric sciencesFlux (metallurgy)Environmental scienceSolar zenith anglePolarConvection cellMeteorologyMaterials sciencePhysicsCombined forced and natural convectionNatural convectionAstronomy

Abstract

fetched live from OpenAlex

Abstract By analyzing a five‐year period (2010–2014) of Defense Meteorological Satellite Program (DMSP) plasma data, we investigated ion upflow occurrence, speed, density, and flux above the polar cap in the northern hemisphere under different solar zenith angle (SZA), solar activity (F10.7), and convection speed. Higher upflow occurrence rates in the dawn sector are associated with regions of higher convection speed, while higher upflow flux in the dusk sector is associated with higher density. The upflow occurrence increases with convection speed and solar activity but decreases with SZA. Upflow occurrence is the lowest when the SZA > 100° and the convection speeds are low. While, the upflow velocity and flux show a clear seasonal dependence with higher speed in the winter and higher flux in the summer during low convection conditions. However, they are detected for the first time to be both higher in summer during high convection conditions. These results suggest that ion upflow in the polar cap is controlled by the combination of convection, solar activity, and solar illumination.

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 score0.130
Threshold uncertainty score0.523

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.013
GPT teacher head0.300
Teacher spread0.288 · 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

Citations23
Published2018
Admission routes1
Has abstractyes

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