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Record W2105554506 · doi:10.5194/angeo-22-4077-2004

Studies of medium scale travelling ionospheric disturbances using TIGER SuperDARN radar sea echo observations

2004· article· en· W2105554506 on OpenAlexaff
L. He, P. L. Dyson, M. L. Parkinson, Weixing Wan

Bibliographic record

VenueAnnales Geophysicae · 2004
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of Alberta
FundersAustralian Research CouncilLa Trobe University
KeywordsIonosphereNoonGeologyDuskGeophysicsMorningRadarLocal timeGeodesyAtmospheric sciencesPhysics

Abstract

fetched live from OpenAlex

Abstract. Seasonal and diurnal variations in the direction of propagation of medium-scale travelling ionospheric disturbances (MSTIDs) have been investigated by analyzing sea echo returns detected by the TIGER SuperDARN radar located in Tasmania (43.4° S, 147.2° E geographic; –54.6°Λ). A strong dependency on local time was found, as well as significant seasonal variations. Generally, the propagation direction has a northward (i.e. equatorward) component. In the early morning hours the direction of propagation is quite variable throughout the year. It then becomes predominantly northwest and changes to northeast around 09:00 LT. In late fall and winter it changes back to north/northwest around 15:00 LT. During the other seasons, northward propagation is very obvious near dawn and dusk, but no significant northward propagation is observed at noon. It is suggested that the variable propagation direction in the morning is related to irregular magnetic disturbances that occur at this local time. The changes in the MSTID propagation directions near dawn and dusk are generally consistent with changes in ionospheric electric fields occurring at these times and is consistent with dayside MSTIDs being generated by the Lorentz force. Key words. Ionosphere (ionospheric disturbances; wave propagation; ionospheric irregularities; signal processing)

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 score0.233
Threshold uncertainty score0.990

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 head0.032
GPT teacher head0.270
Teacher spread0.238 · 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

Citations31
Published2004
Admission routes1
Has abstractyes

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