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Record W1566220736 · doi:10.1002/2014gl062345

Hemispheric asymmetry of the structure of dayside auroral oval

2014· article· en· W1566220736 on OpenAlexaff
Zejun Hu, Yusuke Ebihara, Hongqiao Hu, Beichen Zhang, Binbin Ni, Run Shi, T. Trondsen

Bibliographic record

VenueGeophysical Research Letters · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsMemorial University of Newfoundland
FundersFundamental Research Funds for the Central UniversitiesChinese Arctic and Antarctic AdministrationNational Natural Science Foundation of China
KeywordsAsymmetryIonosphereSolar windGeologyNorthern HemisphereInterplanetary magnetic fieldMagnetosheathAtmospheric sciencesGeophysicsPhysicsMagnetopauseMagnetic field

Abstract

fetched live from OpenAlex

Abstract A comprehensive analysis of long‐term and multispectral auroral observations made in the Arctic and Antarctica demonstrates that the dayside auroral ovals in two hemispheres are both presented in a two‐peak structure, namely, the prenoon 09:00 magnetic local time (MLT) and postnoon 15:00 MLT peaks. The two‐peak structures of dayside ovals, however, are asymmetric in the two hemispheres; i.e., the postnoon average auroral intensity is more than the prenoon one in the Northern Hemisphere but less in the Southern Hemisphere. The hemispheric asymmetry cannot be accounted for by the effect of the interplanetary magnetic field By component and the seasonal difference of ionospheric conductivities in the two hemispheres, which were used to interpret satellite‐observed real‐time auroral intensity asymmetries in the two hemispheres in previous studies. We suggest that the hemispheric asymmetry is the combined effect of the prenoon‐postnoon variations of the magnetosheath density and local ionospheric conductivity.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.263
Teacher spread0.254 · 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 source (direct Gemma or distilled Codex), 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

Citations30
Published2014
Admission routes1
Has abstractyes

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