Two different methodologies for geoid determination from ground and airborne gravity data
Bibliographic record
Abstract
In this study, two methodologies are investigated for geoid determination from ground and airborne gravity data. These two methodologies depend on the downward continuation method used. The first is the inverse Poisson integral; the second is the normal free-air gradient. Each of the two methods requires different treatment of the terrain effects and in turn different approaches to determine the geoid. The two geoid solutions, from ground data, are compared with existing GPS/levelling benchmarks and it is found that the second method gives a better fit due to the bias introduced from the inverse Poisson integral. The same process was applied to the airborne data, but with additional processing, that is the filtering of the terrain effects to preserve the consistency of the data due to the filtering of the airborne data. A study on the effect of filtering was also carried out in this paper and it concluded that filtering the terrain effects has no impact on the geoid. In addition, the airborne data, filtered to three different cut-off frequencies, were used to compute the geoid to investigate the possibility of using the denser data, of lower accuracy, to determine a high-resolution geoid. Even though the data filtered to small cut-off frequency have poorer agreement with the ground data, the geoids computed from the different filtered data is the nearly the same
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".