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Record W276814211 · doi:10.3176/geol.2004.2.01

Effect of the GRACE satellite mission on gravity field studies in Fennoscandia and the Baltic Sea region

2004· article· en· W276814211 on OpenAlexaff
Artu Ellmann

Bibliographic record

VenueProceedings of the Estonian Academy of Sciences Geology · 2004
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysics and Gravity Measurements
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsBaltic seaSatelliteGeodesyField (mathematics)Gravitational fieldGeologyOceanographyEnvironmental scienceClimatologyPhysicsAstronomyMathematics

Abstract

fetched live from OpenAlex

It is customary to utilize the Earth's artificial satellites for detecting long-wavelength components of the Earth's gravity field. The tracking data of the GRACE twin-satellites are the basis of the new geopotential model GGM01, released by the Centre for Space Research at the University of Texas in July 2003. The present paper assesses the quality of the GGM01 model through comparisons with an earlier geopotential model (EGM96). The method of spherical harmonic expansions is used in numerical investigations. The results of evaluation in Fennoscandia and the Baltic Sea region illuminate the discrepancies between the long-wavelength contributions of the models, which may reach several decimetres in the geoidal heights. Thus, even in the gravimetrically well studied area like the Baltic Sea region, the new satellite gravity missions may improve the gravity data significantly. Tests with high-precision GPS-levelling data indicate the offsets between global geoid models and national vertical datums in the Baltic Sea region. The gravity anomaly grid and the GGM01 model are utilized in the computation of the Estonian gravimetric geoid model by the least squares modification of Stokes' formula. The new model EST-03 is fitted to a set of 26 high-precision GPS-levelling points, yielding a root mean square error of 3 cm for the post-fitting residuals. This order of discrepancies is sufficient for many practical and scientific applications.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.887

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.031
GPT teacher head0.282
Teacher spread0.251 · 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 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

Citations2
Published2004
Admission routes1
Has abstractyes

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