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Record W1640793012

Use of RTIGS data streams for validating the performance of the IGS Ultra-Rapid products

2010· article· en· W1640793012 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueEGUGA · 2010
Typearticle
Languageen
FieldEngineering
TopicGNSS positioning and interference
Canadian institutionsnot available
Fundersnot available
KeywordsGlobal Positioning SystemGNSS applicationsComputer scienceKalman filterSatelliteReal-time computingPrecise Point PositioningGeodesyRemote sensingGeographyTelecommunicationsEngineering
DOInot available

Abstract

fetched live from OpenAlex

The IGS (International GNSS Service) Real-Time Working Group (RTIGS) disseminates for several years raw observation data of a globally distributed steady growing station network in real-time via the internet. This observation data can be used for validating the performance of the IGS predicted orbits and clocks (Ultra-Rapid (IGU)). Therefore, based on pre-processed ITRF- station coordinates, clock corrections w.r.t GPS-Time for GPS-satellites and site-receivers as well as satellite orbits are calculated in quasi real-time and compared to the IGU solutions. The Institute for Geodesy and Geophysics of the Technical University of Vienna develops based on the software RTIGS Multicast Receive (RTIGSMR) provided by National Resources Canada (NRCan) the software RTIGU-Control. Using Code-smoothed observations RTIGU-Control calculates in a first step by means of a linear KALMAN-Filter and based on the orbit information of the IGUs real-time clock corrections and clock drifts w.r.t GPS-Time for the GPS-satellites and stations. The second extended KALMAN-Filter (kinematic approach) uses again the Code-smoothed observations corrected for the clock corrections of step 1 to calculate the positions and velocities of the satellites. The calculation interval is set to 30 seconds. The results and comparisons to IGU-products are displayed online but also stored as clock-RINEX- and SP3-files on the ftp-server of the institute, e.g. for validation of the performance of the IGU predicted products. A comparison to the more precise but delayed issued IGS Rapid products (IGR) allows also to validate the performance of RTIGU-Control. To carry out these comparisons the MatLab routine RTIGU-Analyse was established. This routine is for example able to import and process standard clock-RINEX-files of several sources and delivers a variety of comparisons both in graphical or numerical form. Results will become part of this presentation. Another way to analyse the quality and consistency of the RTIGU-Control products is to use them for positioning in post-processing mode. Preliminary results are already available and will also be presented. Further investigations will deal with upgrading RTIGU-Control to become independent of the IGU products. This means to initialize the KALMAN-Filter process using the orbits (and also clocks) from IGU but to use for all further calculation steps the own established orbits. This procedure results in totally independent satellite orbit and clock corrections which could be used for example instead of the broadcast ephemerides in a large number of real-time PPP applications.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.146

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 head0.051
GPT teacher head0.236
Teacher spread0.185 · 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