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Record W2902018380 · doi:10.1029/2018gl080125

Statistical Distributions of Dayside ECH Waves Observed by MMS

2018· article· en· W2902018380 on OpenAlexafffund
Yuequn Lou, Xudong Gu, Danny Summers, Binbin Ni, Kaijun Liu, Song Fu, Zheng Xiang, Zhengyang Zou, Xing Cao, Wenxun Zhang, H. Huang, Ying He

Bibliographic record

VenueGeophysical Research Letters · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsMemorial University of Newfoundland
FundersChinese Academy of SciencesNatural Sciences and Engineering Research Council of CanadaUniversity of Science and Technology of ChinaNational Natural Science Foundation of China
KeywordsMagnetospherePhysicsIonosphereDaytimeAmplitudeGeophysicsLocal timeEarth's magnetic fieldPlasmasphereAtmospheric sciencesMagnetic fieldOptics

Abstract

fetched live from OpenAlex

Abstract Strong electrostatic electron cyclotron harmonic (ECH) waves on the dayside magnetosphere have been reported based on observations of the Magnetospheric Multiscale (MMS) spacecraft. In this study, we analyze high‐quality wave data from the four MMS satellites between 1 September 2015 and 30 August 2018 to investigate the statistical properties of dayside ECH emissions. The results show that dayside ECH waves are preferentially observed on the prenoon side in the outer magnetosphere (L = 8–12), with average wave amplitude Ew > 0.1 mV/m. In addition, besides the typical near‐equatorial (|MLAT| ≤ 15°) region, dayside ECH waves exhibit moderate occurrence rate and wave amplitude in higher latitudinal regions (i.e., 15 < |MLAT| ≤ 40°), possibly due to the off‐equatorial geomagnetic field minimum. Our reported double peaks of dayside ECH wave occurrence zone and considerable occurrence rates of prenoonside ECH waves suggest that dayside ECH waves can be a potentially important contributor to the formation of dayside diffuse aurora.

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 score0.003

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 communication0.0000.000
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 head0.027
GPT teacher head0.309
Teacher spread0.281 · 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

Citations24
Published2018
Admission routes2
Has abstractyes

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