THE UNIVERSITY OF CALGARY Some Investigations on Local Geoid Determination from Airborne Gravity Data
Bibliographic record
Abstract
Advances in the Global Positioning System (GPS) and strapdown Inertial Navigation System (INS) have played a significant role in the development of airborne gravimeters. Previous studies have shown that the airborne gravity data obtained from these gravimeters have very good quality. In this thesis some possible procedures to determine the geoid from airborne gravity data are studied. Different practical issues are investigated: the digital terrain model (DTM) resolution needed to quantify the topography at the flight level, the need for terrain effects filtering, and the use of two downward continuation procedures. The results showed that for a geoid of resolution 5 � � 5�, a DTM of 30 arcsec can be safely used, and in benign topography even a 60 arcsec could be used. Although filtering is essential from the theoretical point of view, practically it is not important. The geoid determined from airborne gravity data, downward continued to the reference sphere using the normal free-air gradient, shows a better agreement with the reference geoid (computed from ground gravity data) than using the inverse Poisson integral. The geoid determined from airborne gravity agreed
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".