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Record W3090971655 · doi:10.3390/jmse8100764

SBAS/EGNOS for Maritime

2020· article· en· W3090971655 on OpenAlexfundno aff
Manuel López, José-Manuel Álvarez, J.M. Lorenzo, Carlos Garcia Daroca

Bibliographic record

VenueJournal of Marine Science and Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicGNSS positioning and interference
Canadian institutionsnot available
FundersInstitute of Musculoskeletal Health and ArthritisEuropean GNSS Agency
KeywordsGNSS augmentationGNSS applicationsGlobal Positioning SystemComputer scienceAir navigationSatellite navigationReceiver autonomous integrity monitoringTelecommunicationsRemote sensingGeography

Abstract

fetched live from OpenAlex

The Global Navigation Satellite System (GNSS) has become the primary means of obtaining Position, Navigation, and Timing (PNT) information at sea. The current capabilities of the Global Positioning System (GPS) constellation, although adequate for ocean navigation, have some shortfalls for coastal navigation: some user communities have a need for enhanced performance and they can benefit from the available “augmentation” techniques, resulting in improved GPS performance. Nowadays, the users can take advantage of Satellite-Based Augmentation Systems (SBASs). The maritime domain has been used SBAS for several years and it is supported by GNSS receivers used in the recreational and professional sectors. The SBAS/European Geostationary Navigation Overlay Service (EGNOS) can be used to complement the differential GNSS (DGNSS) for the provision of enhanced accuracy and integrity information with additional benefits. There are different possible solutions for the transmission of SBAS/EGNOS information to maritime users, considering that the corrections can be available from different transmission means. The different options for the use of SBAS for maritime navigation, the benefits brought to mariners, as well as the associated regulations, standardization and service provision aspects, are presented in this article.

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 score0.636
Threshold uncertainty score0.245

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 head0.010
GPT teacher head0.198
Teacher spread0.188 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

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