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Record W2171371526 · doi:10.5194/angeo-32-147-2014

Variations in the occurrence of SuperDARN F region echoes

2014· article· en· W2171371526 on OpenAlexaffabout
M. Ghezelbash, Robyn Fiori, A. V. Koustov

Bibliographic record

VenueAnnales Geophysicae · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsNatural Resources CanadaUniversity of Saskatchewan
Fundersnot available
KeywordsNoonSouthern HemisphereNorthern HemisphereIonosphereGeologyMidnightRadarSolar maximumSolsticeSolar minimumEquinoxLocal timeAtmospheric sciencesSolar cycleClimatologyGeophysicsAstronomyPhysicsGeodesyLatitudeSolar windMagnetic field

Abstract

fetched live from OpenAlex

Abstract. The occurrence of F region ionospheric echoes observed by a number of SuperDARN HF radars is analyzed statistically in order to infer solar cycle, seasonal, and diurnal trends. The major focus is on Saskatoon radar data for 1994–2012. The distribution of the echo occurrence rate is presented in terms of month of observation and magnetic local time. Clear repetitive patterns are identified during periods of solar maximum and solar minimum. For years near solar maximum, echoes are most frequent near midnight during winter. For years near solar minimum, echoes occur more frequently near noon during winter, near dusk and dawn during equinoxes and near midnight during summer. Similar features are identified for the Hankasalmi and Prince George radars in the northern hemisphere and the Bruny Island TIGER radar in the southern hemisphere. Echo occurrence for the entire SuperDARN network demonstrates patterns similar to patterns in the echo occurrence for the Saskatoon radar and for other radars considered individually. In terms of the solar cycle, the occurrence rate of nightside echoes is shown to increase by a factor of at least 3 toward solar maximum while occurrence of the near-noon echoes does not significantly change with the exception of a clear depression during the declining phase of the solar cycle.

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 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 score0.411
Threshold uncertainty score0.275

Codex and Gemma teacher scores by category

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

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 head0.008
GPT teacher head0.222
Teacher spread0.214 · 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

Citations17
Published2014
Admission routes2
Has abstractyes

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