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Record W2769821060 · doi:10.1002/2017gl074678

The Solar Flux Dependence of Ionospheric 150 km Radar Echoes and Implications

2017· article· en· W2769821060 on OpenAlexaff
A. K. Patra, P. Pavan Chaitanya, Yuichi Otsuka, T. Yokoyama, M. Yamamoto

Bibliographic record

VenueGeophysical Research Letters · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsIonosphereFlux (metallurgy)RadarPhysicsExtreme ultraviolet lithographyAmplitudeGeophysicsPlasmaAtmospheric sciencesComputational physicsDaytimeAstrophysicsOpticsMaterials science

Abstract

fetched live from OpenAlex

Abstract Radar echoes from the daytime equatorial ionospheric F1 region, popularly known as “150 km echoes,” have challenged ionospheric plasma physicists for several decades. Recent theoretical simulations showed that enhanced photoelectron fluxes can amplify the amplitude of plasma waves, generating spectra similar to those of the radar echoes, implying that larger solar fluxes should produce more frequent and stronger 150 km echoes. Inspired by this proposal, we studied the occurrence and intensity dependence of the echoes on the EUV flux observed by SOHO over several years. The occurrence and intensity of the echoes were found to have an inverse relationship with this EUV flux measurement. The multiyear trend is independent of the variability often observed over successive days with nearly identical EUV fluxes. These results imply that the relationship between the echoes and EUV flux is more complex. We propose that gravity waves modulate the amplitude of 150 km echoes through changes in the variations in plasma density and photoelectron fluxes associated with the gravity wave‐induced neutral density modulations.

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 score0.001
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
Bibliometrics0.0000.000
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 head0.019
GPT teacher head0.304
Teacher spread0.284 · 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

Citations14
Published2017
Admission routes1
Has abstractyes

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