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High-Precision Ionospheric TEC Recovery Using a Regional-Area GPS Network

2001· article· en· W2105344126 on OpenAlex
Xiangqian Liao, Yang Gao

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNAVIGATION Journal of the Institute of Navigation · 2001
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsTECGlobal Positioning SystemIonosphereTotal electron contentGeodesyRemote sensingSatelliteComputer scienceGeologyTelecommunicationsGeophysicsPhysics

Abstract

fetched live from OpenAlex

ABSTRACT: A new method of ionospheric total electron content (TEC) recovery has been developed and is described in this paper. It focuses on recovering ionospheric vertical TEC at centimeter accuracy using carrier phase as the principal observable from regional-area GPS networks. To eliminate satellite- and receiver-dependent biases, the double-difference technique is applied to derive reference network ionospheric measurements. A grid model with a moving window is used along with a streamlined Kalman filter to model and estimate the absolute vertical ionospheric TEC across the GPS network. Numerical tests have been carried out to assess the attainable accuracy of the ionosphere estimates using data from an operational GPS network. The results confirm that regional ionospheric TEC estimates can be determined at an accuracy of several centimeters.

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.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.749
Threshold uncertainty score0.624

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.015
GPT teacher head0.244
Teacher spread0.229 · 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