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Record W2498633402

Positioning accuracy and availability analysis of three commercial WADGPS services

2000· article· en· W2498633402 on OpenAlexvenueaboutno aff
Yueyuan Gao, Julie A. Anderson, B. Banadyga, L. Wang

Bibliographic record

VenueGEOMATICA · 2000
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Computational Techniques and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsGeographyForestryHumanitiesCartographyArt
DOInot available

Abstract

fetched live from OpenAlex

Plusieurs services commerciaux du WADGPS (GPS differentiel a couverture etendue) sont fonctionnels a l'heure actuelle et fournissent des services de correction du DGPS (GPS differentiel) a l'echelle continentale en Amerique du Nord et a travers le monde. Etant donne le vaste choix disponible aux usagers du GPS, on a realise une etude sur le rendement de trois importants services de correction, soit le LANDSTAR, l'OMNISTAR et le SATLOC dans un cadre fonctionnel. Les resultats de cette etude font l'objet du present article. L'analyse comprend une evaluation de l'exactitude du positionnement et, en particulier, de la disponibilite des corrections differentielles pour chaque systeme verifie. On a fait l'etude a Calgary, Les resultats demontrent un niveau comparable d'exactitude du positionnement pour les trois services du WADGPS mais avec des disponibilites de positionnement differentes. Les resultats presentes sont utiles pour les usagers du WADGPS en leur permettant de prendre une decision eclairee dans le choix des divers services disponibles.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.120

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.012
GPT teacher head0.273
Teacher spread0.262 · 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 designObservational
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

Citations1
Published2000
Admission routes2
Has abstractyes

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Same venueGEOMATICASame topicAdvanced Computational Techniques and ApplicationsFrench-language works237,207