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Record W2549127733

Geoid Determination Using Different Gravity Reduction Techniques

2001· article· en· W2549127733 on OpenAlexaffabout
S. Bajracharya, C. Kotsakis

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysics and Gravity Measurements
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsGeoidGeodesyUndulation of the geoidGeologyReduction (mathematics)GravimetryGravitational fieldGlobal Positioning SystemBouguer anomalyGravity anomalyGeophysicsMathematicsGeometryComputer sciencePhysicsClassical mechanics
DOInot available

Abstract

fetched live from OpenAlex

Abstract: The topographical masses outside the geoid have to be removed completely for its determination using Stokes’s boundary value problem (BVP) approach. The mathematical and the physical treatment of this problem play an important role in the computation of a precise (local or regional) gravimetric geoid solution. There are various gravity reduction techniques used in physical geodesy to treat this problem. Bouguer reduction, residual terrain model (RTM) reduction, Airy-Heiskanen (AH), Pratt-Hayford (PH) and Vening Meinesz isostatic models, and the Helmert condensation method are mostly discussed. One of the most rugged areas of Canadian Rockies, which lies in latitude between 49N and 54N and in longitude between 124W and 114W, is selected to compute different gravimetric geoid solutions using the AH and PH topographic-isostatic reduction techniques, Helmert’s second condensation method and the Rudzki method. The geoid is computed from Stokes’s integral formula with the rigorous spherical kernel by the one dimensional fast Fourier transform algorithm, and the OSU91A model as reference global field. A digital terrain model of 15×15 arc seconds is used to compute the effect of topography and its indirect effect for the different reduction schemes. The results obtained from the gravimetric geoid solutions are finally compared with the GPS-levelling derived geoid undulations of this area.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.533
Threshold uncertainty score0.468

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 head0.039
GPT teacher head0.250
Teacher spread0.212 · 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
Published2001
Admission routes2
Has abstractyes

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