On Downward Continuing Airborne Gravity Data for Local Geoid Modeling
Bibliographic record
Abstract
The theories of downward continuation (DC) have been extensively studied for many decades, during which many different approaches were developed. In real applications, however, researchers often just use one method, probably due to resource limitations or to finish their work, without a rigorous head-to-head comparison with other alternatives. Considering that different methods perform quite differently under various conditions, comparing results from different methods can help a lot for identifying potential problems when dramatic differences occur, and for confirming the correctness of the solutions when results converge together, which is extremely important for real applications such as building official national vertical datums. This paper gives exactly such a case study by recording the collective wisdom recently developed within the IAG’s study group SC2.4.1. A total of six normally used DC methods, which are SHA (NGS), LSC (DTU Space), Poisson and ADC (NRCan), RBF (DU Delft), and RLSC (TUM), are applied to both simulated data (in the combination of two sampling strategies with three noise levels) and real data in a Colorado-area test bed. The data are downward continued to both surface points and to the reference ellipsoid surface. The surface points are directly evaluated with the observed gravity data on the topography. The ellipsoid points are then transformed into geoid heights according to NRCan’s Stokes-Helmert’s scheme and eventually evaluated at the GNSS/Leveling benchmarks. In this presentation, we will summarize the work done and results obtained by the aforementioned workgroup.
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 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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".