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Record W3025270682 · doi:10.4095/326084

An investigation on the GNSS ionospheric mapping-functions uncertainties using NeQuick model

2020· report· en· W3025270682 on OpenAlexaff
Reza Ghoddousi‐Fard

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEngineering
TopicGNSS positioning and interference
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsGNSS applicationsIonosphereEnvironmental scienceRemote sensingComputer scienceGeographyGlobal Positioning SystemGeologyTelecommunicationsGeophysics

Abstract

fetched live from OpenAlex

Commonly used two-dimensional ionospheric models for GNSS positioning applications, including Total Electron Content (TEC) maps, require a mapping function (obliquity factor) which is used for conversion between vertical and slant TEC at ionospheric pierce points. In this paper, NeQuick, a three-dimensional semi-empirical model, is used to simulate the level of uncertainties that one may expect from more simplified approaches. In order to evaluate the performance of mapping functions on GNSS vertical TEC estimation, coinciding pierce points from mixed stations and receivers from stations over North America are analyzed. A fit to the NeQuick derived mapping function values resulted in an empirical mapping function which performed slightly better than commonly used mapping functions during the studied periods and locations.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.906
Threshold uncertainty score0.930

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 integrity0.0000.001
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 head0.127
GPT teacher head0.267
Teacher spread0.140 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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