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Record W2039974433 · doi:10.2478/arsa-2013-0010

Analysis of Current Position Determination Accuracy in Natural Resources Canada Precise Point Positioning Service

2013· article· en· W2039974433 on OpenAlexaboutno aff
Grzegorz Krzan, Karol Dawidowicz, Krzysztof Świątek

Bibliographic record

VenueArtificial Satellites · 2013
Typearticle
Languageen
FieldEngineering
TopicGNSS positioning and interference
Canadian institutionsnot available
Fundersnot available
KeywordsPrecise Point PositioningGNSS applicationsGLONASSGlobal Positioning SystemComputer scienceSatellitePosition (finance)Real Time KinematicGeodesySatellite navigationReal-time computingGalileo (satellite navigation)Orbit (dynamics)Remote sensingService (business)TelecommunicationsGeographyAerospace engineeringEngineering

Abstract

fetched live from OpenAlex

Abstract Precise Point Positioning (PPP) is a technique used to determine highprecision position with a single GNSS receiver. Unlike DGPS or RTK, satellite observations conducted by the PPP technique are not differentiated, therefore they require that parameter models should be used in data processing, such as satellite clock and orbit corrections. Apart from explaining the theory of the PPP technique, this paper describes the available web-based online services used in the post-processing of observation results. The results obtained in the post-processing of satellite observations at three points, with different characteristics of environment conditions, using the CSRS-PPP service, will be presented as the results of the experiment. This study examines the effect of the duration of the measurement session on the results and compares the results obtained by working out observations made by the GPS system and the combined observations from GPS and GLONASS. It also presents the analysis of the position determination accuracy using one and two measurement frequencies

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.470
Threshold uncertainty score0.919

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 communication0.0000.000
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 head0.012
GPT teacher head0.234
Teacher spread0.223 · 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 designSimulation or modeling
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

Citations8
Published2013
Admission routes1
Has abstractyes

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