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Record W2076122631 · doi:10.1186/bf03352332

The impact of geomagnetic substorms on GPS receiver performance

2014· article· en· W2076122631 on OpenAlexaffabout
S. Skone, M. de Jong

Bibliographic record

VenueEarth Planets and Space · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsSubstormGlobal Positioning SystemEarth's magnetic fieldIonosphereGps receiverGeomagnetic stormScintillationGeodesyTracking (education)MeteorologyEnvironmental scienceInterplanetary scintillationRemote sensingGeologyPhysicsGeophysicsAssisted GPSComputer scienceMagnetosphereCoronal mass ejectionTelecommunicationsSolar windMagnetic field

Abstract

fetched live from OpenAlex

During the current period of solar maximum, there is concern within the GPS community regarding GPS receiver performance during periods of intense geomagnetic substorms. Such storms are common in the high latitude auroral region, and are associated with small-scale scintillation effects, which can cause receiver tracking errors and loss of phase lock. The auroral oval can extend many degrees equatorward under active ionospheric conditions, with an impact on precise positioning applications in Canada, the United States and Northern Europe. In this paper, a study of receiver tracking performance is conducted during periods of auroral substorm activity. Dual frequency observations are obtained using codeless and semicodeless GPS receivers (Trimble 4000SSi, NovAtel MiLLennium and Ashtech Z-12), and performance comparisons are established and interpreted with respect to GPS availability at solar maximum and the years beyond.

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)0.002
metaresearch head score (Gemma)0.009
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
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 head0.003
GPT teacher head0.193
Teacher spread0.189 · 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

Citations56
Published2014
Admission routes2
Has abstractyes

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