MétaCan
Menu
Back to cohort
Record W2066915909 · doi:10.1515/jag.2011.004

Impact of second-order ionospheric delay on GPS precise point positioning

2011· article· en· W2066915909 on OpenAlex

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

VenueJournal of Applied Geodesy · 2011
Typearticle
Languageen
FieldEngineering
TopicGNSS positioning and interference
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsPseudorangeGlobal Positioning SystemGeodesyPrecise Point PositioningIonosphereGroup delay and phase delaySatelliteResidualGPS signalsPhysicsComputer scienceAssisted GPSGeographyTelecommunicationsGNSS applicationsAlgorithmGeophysicsBandwidth (computing)

Abstract

fetched live from OpenAlex

Traditionally, in GPS precise point positioning (PPP), ionosphere-free linear combinations of dual-frequency carrier-phase and pseudorange measurements are used. Unfortunately, with these linear combinations only the first-order ionospheric delay term is removed and higher order ionospheric delay terms are usually not taken into account. Such residual error components may deteriorate the PPP solution and slow down the convergence time. In this paper the second-order ionospheric delay term is modeled and it is shown that its effect on GPS satellite orbit varies from 2.3 mm to 23.8 mm in the radial direction, 3.6 mm to 18.8 mm in the along-track direction and 2 mm to 16.3 mm in the cross-track direction. In addition, GPS satellite clock corrections showed a difference of up to 0.067 ns.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.589
Threshold uncertainty score0.873

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)0.0010.000

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 head0.012
GPT teacher head0.220
Teacher spread0.208 · 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