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Record W2151484977 · doi:10.5539/mas.v7n6p1

Generation of Seismic-Related DC Electric Fields and Lithosphere-Atmosphere-Ionosphere Coupling

2013· article· en· W2151484977 on OpenAlexvenueno aff
В. М. Сорокин, Masashi Hayakawa

Bibliographic record

VenueModern Applied Science · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicEarthquake Detection and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsIonosphereElectric fieldAtmosphere (unit)GeophysicsLithosphereCoupling (piping)Ionospheric heaterAtmospheric sciencesElectromagnetic fieldPhysicsField (mathematics)GeologyComputational physicsMeteorologyMaterials scienceSeismology

Abstract

fetched live from OpenAlex

This paper reviews modeling of the influence of earthquake (EQ) preparation processes on the ionosphere through the electric field and electric current occurring in the global atmosphere–ionosphere electric circuit. Our consideration is based on the satellite-and ground-based experimental data of electric fields, plasma and electromagnetic perturbations obtained for several days before an EQ. We have ruled out the models which are not consistent with the experimental data on the electric fields in the ionosphere and also on the ground surface. There has then been proposed a new model of the generation of electric field on the basis of injection of charged aerosols into the atmosphere, and we discuss the mechanism of lithosphere-atmosphere-ionosphere coupling. It is then shown that such changes in the electric field within the ionosphere induce a variety of plasma and electromagnetic phenomena associated with an impending EQ.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.014
GPT teacher head0.198
Teacher spread0.184 · 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

Citations73
Published2013
Admission routes1
Has abstractyes

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