Comparison of Remove-Compute-Restore and University of New Brunswick Techniques to Geoid Determination over Australia, and Inclusion of Wiener-Type Filters in Reference Field Contribution
Bibliographic record
Abstract
The commonly adopted remove-compute-restore (RCR) technique for regional gravimetric geoid determination uses the maximum degree of a combined global geopotential model and regional gravity data via the spherical Stokes integral. The University of New Brunswick’s (UNB) technique involves the use of a deterministically modified integration kernel, a degree-20 satellite-only reference field, integration of high-frequency terrestrial gravity anomalies over a spherical cap of 6° radius about each computation point, and a separate computation of the truncation bias used Degrees 21–120 of a combined global geopotential model. Both approaches are tested over Australia and the resulting geoid models compared with a nationwide dataset of 1,013 Global Positioning System (GPS)-leveled points, and with the most recent Australian geoid model, AUSGeoid98. A subsequent experiment considers the commission errors in the reference field used by applying a Wiener-type filter based on the global degree- and error-degree variances of the EGM96 combined and EGM96S satellite-only global geopotential models. The theoretical basis of this adapted approach will be presented, together with comparisons of the resulting geoid solution with the 1,013 GPS-leveling data, UNB, RCR, and AUSGeoid98 solutions.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".