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Record W2171851626 · doi:10.5194/angeo-30-235-2012

Velocity of E-region HF echoes under strongly-driven electrojet conditions

2012· article· en· W2171851626 on OpenAlexafffund
J. D. Gorin, A. V. Koustov, R. A. Makarevich, Satonori Nozawa

Bibliographic record

VenueAnnales Geophysicae · 2012
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaNational Institute of Polar ResearchDeutsche Forschungsgemeinschaft
KeywordsElectrojetIonosphereElectric fieldPhysicsRadarDrift velocityGeologyGeodesyAzimuthEquatorial electrojetComputational physicsMagnetic fieldGeophysicsOpticsEarth's magnetic field

Abstract

fetched live from OpenAlex

Abstract. Data collected by the Stokkseyri SuperDARN HF radar simultaneously at short and far ranges are used to investigate the relationship between the velocity of E-region HF echoes, E×B electron drift and the isothermal ion-acoustic speed CS. The work targets large E×B drifts of >1000 m s−1 and observations predominantly along the flow. By considering the EISCAT temperature and electric field data, an empirical relationship between the E×B drift velocity and CS is established for a number of ionospheric heights. For the Stokkseyri HF radar beams oriented roughly along the E×B direction, the observed E-region HF velocities are consistent with the CS values at the bottom of the electrojet but not at its center. For a subset of the data with smooth and consistent velocity variation with the beam azimuth at both short and far radar ranges the velocity varies according to the cosine law. For the E-region echoes, the proportionality coefficient in the cosine law is consistent with the CS values at the bottom of the electrojet. For these events, the E-region velocity maximum is shown to be between the E×B and electric field directions. The statistically average shift is ~20° and it increases slightly with the E×B magnitude.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.732
Threshold uncertainty score0.734

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 head0.011
GPT teacher head0.246
Teacher spread0.234 · 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 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

Citations20
Published2012
Admission routes2
Has abstractyes

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