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Record W2440588454 · doi:10.1080/00396265.2016.1180798

Tropospheric delay modelling for the EGNOS augmentation system

2016· article· en· W2440588454 on OpenAlexaff
Kamil Kaźmierski, Marcelo C. Santos, Jarosław Bosy

Bibliographic record

VenueSurvey Review · 2016
Typearticle
Languageen
FieldEngineering
TopicGNSS positioning and interference
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsDepth soundingTroposphereLimitingSatelliteRemote sensingSatellite systemEnvironmental scienceMeteorologyAtmosphere (unit)Computer scienceGlobal Positioning SystemGNSS applicationsGeodesyGeographyTelecommunicationsAerospace engineeringCartographyEngineering

Abstract

fetched live from OpenAlex

Tropospheric delay is one of the deleterious factors limiting the accuracy of the precise Global Navigation Satellite Systems positioning. The value of delay depends on the path through which a signal has to follow in the subsurface layers of the atmosphere. Tropospheric delay models are developed to overcome this limitation. Among them one can find UNB, TropGrid or IGGtrop models. In this paper, we adjusted the UNB3m model to the actual meteorological parameters from Europe. A new model was called UNBe.eu covering the EGNOS augmentation system area. The use of meteorological observations helped us to decrease the bias for more than 70% of reference radio sounding locations. Still, 30% of reference sites depicted a lack of any improvements of the ZTD estimation with regard to the newly established model. Therefore, this study puts forward a need for a deeper investigation of the discussed issue.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.021
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.052
GPT teacher head0.258
Teacher spread0.206 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations5
Published2016
Admission routes1
Has abstractyes

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