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Record W2890207660 · doi:10.5194/angeo-36-1657-2018

Validation of Clyde River SuperDARN radar velocity measurements with the RISR-C incoherent scatter radar

2018· article· en· W2890207660 on OpenAlexaffabout
A. V. Koustov, R. G. Gillies, Peter Bankole

Bibliographic record

VenueAnnales Geophysicae · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of CalgaryUniversity of Saskatchewan
Fundersnot available
KeywordsRadarGeologyBackscatter (email)Incoherent scatterConvectionPlasmaGeophysicsGeodesyMeteorologyPhysicsIonosphere

Abstract

fetched live from OpenAlex

Abstract. The study considers simultaneous plasma velocity measurements in the eastward direction carried out by the Clyde River (CLY) Super Dual Auroral Radar Network (SuperDARN) high-frequency (HF) radar and Resolute Bay (RB) incoherent scatter radar – Canada (RISR-C). The HF velocities are found to be in reasonable agreement with RISR velocities up to magnitudes of 700–800 m s−1 while, for faster flows, the HF velocity magnitudes are noticeably smaller. The eastward plasma flow component inferred from SuperDARN convection maps (constructed for the area of joint measurements with consideration of velocity data from all the radars of the network) shows the effect of smaller HF velocities more notably. We show that the differences in eastward velocities between the two instruments can be significant and prolonged for observations of strongly sheared plasma flows.

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 score0.483
Threshold uncertainty score0.602

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 head0.013
GPT teacher head0.226
Teacher spread0.213 · 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

Citations1
Published2018
Admission routes2
Has abstractyes

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