MétaCan
Menu
← Back to cohort
Record W2894539271 · doi:10.5539/apr.v10n5p57

Effects of Height of F2-Layer on Critical Frequency by Use of Data at Ouagadougou Station

2018· article· en· W2894539271 on OpenAlexvenueno aff
Emmanuel Nanéma, Konaté Moustapha, Doua Allain Gnabahou, Frédéric Ouattara

Bibliographic record

VenueApplied Physics Research · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsIonosphereCritical frequencyDaytimeAtmospheric sciencesRadio waveEnvironmental scienceReflection (computer programming)Ionospheric reflectionSolar cycle 22International Reference IonosphereMeteorologySolar cycleAtmosphere (unit)PhysicsIonospheric absorptionGeophysicsPlasmaTotal electron contentComputer scienceSolar wind

Abstract

fetched live from OpenAlex

Ionosphere investigation leads to the knowledge of its composition in particles. The particle density and composition determine the capacity of this region to reflect radio waves in the atmosphere at different heights. Some variables such as season, solar cycle phase also influence the ionosphere behavior. Radio waves frequencies pass through the ionosphere layer without reflection above a critical value determining the critical frequency. This study determines the critical frequency of radio waves in the F2 layer (foF2) of the ionosphere by use of data at Ouagadougou station during the minimum and the maximum of solar cycle 22, at different seasons with the height of F2-layer (hmF2). Daytime and nighttime also influence ionosphere parameters. The study presents the hourly behavior of foF2 according to hmF2 values.

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)0.002
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 score0.018
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0000.000

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 head0.069
GPT teacher head0.369
Teacher spread0.300 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueApplied Physics Research→Same topicIonosphere and magnetosphere dynamics→French-language works237,207→