Geoid Determination Using Different Gravity Reduction Techniques
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".