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Record W2743117224 · doi:10.6000/1927-5129.2017.13.69

Investigation in the Variations of Ionospheric f0F2 due to Sunspot Numbers over Wakkanai using EDA Technique

2017· article· en· W2743117224 on OpenAlexvenueno aff
Muzammil Mushtaq Hussain, Syed Nazeer Alam, Faisal Ahmed Khan Afridi

Bibliographic record

VenueJournal of Basic & Applied Sciences · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsSunspot numberMidnightNoonSunspotIonosphereLatitudeUnivariateAtmospheric sciencesEnvironmental scienceMeteorologyBivariate analysisStandard deviationClimatologySolar cycleMathematicsGeologyGeodesyGeographyStatisticsPhysicsMultivariate statistics

Abstract

fetched live from OpenAlex

In this research article, the authors have implemented the Exploratory Data Analysis (EDA) techniques to examine the deviation of the monthly median noon and midnight values of the critical frequency of F2 layer ionosphere (i.e. f0F2) at the Wakkanai station (45.39°N, 141.68°E), Japan, during sunspot cycle 21st (1976-1986) and 23rd (1996-2008). Primarily, univariate analysis has been done, which shows the variations in f0F2 at different local times, seasons and in the range of sunspot numbers (SSN), in which winter and semi-annual anomalies are detected in the months of December and March respectively. Secondly, the regression analysis is being used as a bivariate data analysis. The results proved a significantly nonlinear relationship exists between f0F2 and SSN. In both solar cycles, saturation effects are seen in the month of March during the noontime period and immensely in June during the midnight time. The behavior of the ionosphere has been studied for different latitudes, seasonal effects and sunspot dynamic conditions, in which this paper plays an essential role in it.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.284
Threshold uncertainty score0.277

Codex and Gemma teacher scores by category

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

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 head0.021
GPT teacher head0.273
Teacher spread0.252 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations0
Published2017
Admission routes1
Has abstractyes

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