MétaCan
Menu
Back to cohort
Record W2772593330 · doi:10.15625/0866-7187/40/1/10914

Improvement of the accuracy of the quasigeoid model VIGAC2017

2017· article· en· W2772593330 on OpenAlexaboutno aff
Ha Minh Hoa

Bibliographic record

VenueVietnam Journal of Earth Sciences · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysics and Gravity Measurements
Canadian institutionsnot available
Fundersnot available
KeywordsGeodesyGlobal Positioning SystemGeologyCollocation (remote sensing)Remote sensingComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

As mentioned in (Ha Minh Hoa, 2017), a national spatial reference system will be constructed based on a highly accurate national quasigeoid model with accuracy more than 4 cm. In Vietnam at the present stage there isn’t a detailed gravimetric measurement in mountainous regions and marine area. So with the purpose of improvement of accuracy of the national quasigeoid model VIGAC2017, we only can solve the task of fitting this model to national quasigeoid heights obtained from heights GPS/first, second orders levelling quasigeoid heights through least squares collocation.This scientific article will introduce a first research result for improvement of accuracy of the quasigeoid model VIGAC2017 on the base of it’s fitting to 194 national quasigeoid heights by the least squares collocation. Research results show that accuracy of the quasigeoid model VIGAC2017 will be obtained at level of ±0.058 m and increased to 20.69 %.ReferencesCressie N.A.C., 1993. Statistics for spatial data, John Wiley & Sons. New York, 900p.Ha Minh Hoa, et al., 2012. Research scientific base for perfection of the height system in connection with construction of national dynamic reference system. General report of the science - technological teme of the Ministry of Natural Resources and Environment, Hanoi, 247p.Ha Minh Hoa (Editor), 2016. Research for determination of normal surfaces of sea levels (“zero” depth surface, mean sea surface, highest sea surface) by methods of geodesy, hydrography and geology with serving construction of buildings and planning of coastline in tendency of climate changes”. State techno - scientific theme with code KC.09.19/11-15 in period of 2011-2015, Vietnam Ministry of Science and Technology, Hanoi, 563p. Huang J., Véronneau M., 2013. Contribution of the GRACE and GOCE models to a geopotential - based geodetich vertical datum in Canada. Geophysical Research Abstracts, 15, EGU2013-10164.Iliffe J.C., Ziebart M., Cross P.A., Forsberg R., Strykowski G., Tscherning C.C., 2003. OSGM02: A New model for converting GPS-derived heights to local height datums in Great Britain and Ireland. Survey Review, 37(290), 276-293.Marcin Ligas, Marek Kulczycki, 2014. Kriging approch for local height transformations. J, Geodesy And Cartography, Polish Academy of Sciences, 63(1), 5-37, Doi: 10,2478/geocart-2014-0002.Metin Soycan, 2014. Improving EGM2008 by GPS and leveling data at local scale. BCG - Boletin de Ciências Geodésicas Sec, Artigos, Curitiba, 20(1), 3-18, on - lineversion, ISSN 1982-2170. Doi,org/10,1590/S1982-21702014000100001.Moritz H., 1980. Advanced Physical Geodesy. Herbert wichmann Verlag Karlsruhe, Abacus Press Tunbridge Wells Ken, 512p. Quasigeoid of the Federal Republic of Germany GCG2016. Federal Agency for Cartography and Geodesy, www,geodatenzentrum.de.Roman D.R., Wang Y.M., Saleh J., Li X., 2010. Geodesy, Geoids & Vertical Datums: A Perspective from the U,S, National Geodetic Survey. FIG Congress 2010, Sydney, Australia, April 2010, 11-16.Schabenger O., Gotway C.A., 2005. Statistical methods for spatial data analysis. Chapman & Hall/CRC, New York, ISBN 1-58488-322-7, 488p. Smirnov N.V., Belugin D.A., 1969. Probability theory and mathematical statistics in applying to geodesy. Moscow, Nedra, 379p.

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)0.003
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: Empirical · Consensus signal: none
Teacher disagreement score0.038
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.044
GPT teacher head0.264
Teacher spread0.220 · 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
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
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueVietnam Journal of Earth SciencesSame topicGeophysics and Gravity MeasurementsFrench-language works237,207