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Record W2996298253 · doi:10.5539/apr.v12n1p38

One-Day-Shock, Two-Days-Shock and Three-Days-Shocks foF2 Diurnal Variation with Solar Cycle Phases at Ouagadougou Station from 1966 to 1998. Comparison with IRI 2012 Predictions

2019· article· en· W2996298253 on OpenAlexvenueno aff
Abdoul-kader SEGDA, Doua Allain Gnabahou, Frédéric Ouattara

Bibliographic record

VenueApplied Physics Research · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsShock (circulatory)IonosphereSolar minimumDaytimeSolar cycleAtmospheric sciencesPhysicsMeteorologyDiurnal temperature variationEnvironmental sciencePlasmaGeophysicsSolar wind

Abstract

fetched live from OpenAlex

The present work concerns foF2 time variation at Ouagadougou station for three solar cycles (from cycle 20 to cycle 22). We not only investigate solar cycle phase dependence under shock activity that is divided into one-shock-activity, two-shock-activity and three-shock-activity but also compare the IRI 2012 model values with the data carried out at Ouagadougou station. This study reveals that there is no one-day-shock during solar minimum phase. For the other solar cycle phases IRI 2012 reproduces the ionosphere electrodynamics at daytime except during the increasing phase. During night time the model is not suitable. The best subroutine under one-day-shock activity is URSI for increasing and decreasing phases. During the maximum phase it is CCIR. For two-days-shock activity IRI 2012 reproduces the ionosphere electrodynamics during the minimum and the increasing phases. The best subroutine is CCIR during the minimum phase and URSI for the other solar cycle phases. For three-days-shock activity IRI 2012 is not suitable. The best model is URSI for all solar cycle phases.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.366
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.020
GPT teacher head0.289
Teacher spread0.268 · 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.

Study designSimulation or modeling
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

Citations1
Published2019
Admission routes1
Has abstractyes

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