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Record W2085086480 · doi:10.1029/2007gl032164

First radar measurements of ionospheric electric fields at sub‐second temporal resolution

2008· article· en· W2085086480 on OpenAlexaboutno aff
R. A. Greenwald, Kjellmar Oksavik, R. J. Barnes, J. M. Ruohoniemi, J. B. H. Baker, E. R. Talaat

Bibliographic record

VenueGeophysical Research Letters · 2008
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsSubstormIonosphereRadarDoppler effectGeophysicsElectric fieldRemote sensingTemporal resolutionGeologyDoppler radarMagnetometerMagnetospherePhysicsGeodesyMagnetic fieldOptics

Abstract

fetched live from OpenAlex

A new multipulse sounding technique currently being used at the Wallops Island and Goose Bay SuperDARN radars has produced significant improvements in the temporal resolution of Doppler velocity measurements from which plasma velocities and electric fields are determined. The new technique allows Doppler velocities to be determined from every 200 ms multipulse sequence transmitted by the radar (equivalent to a 5 Hz measurement rate). To our knowledge, this is the highest Doppler measurement rate that has ever been attained with ionospheric radars. Tests of the new technique with the Wallops radar and Ottawa magnetometer revealed bursts of subauroral electric and magnetic field pulsations with periods of 13–20 s during a substorm expansion phase. These results indicate that SuperDARN measurements can be used to study highly dynamic processes in the coupled magnetosphere‐ionosphere system, including storm and substorm electrodynamics, short‐period pulsations and short‐term variability in Joule heating.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.032
GPT teacher head0.265
Teacher spread0.233 · 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

Citations16
Published2008
Admission routes1
Has abstractyes

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