Comparison of airborne gravity processing results by GravPRO and Newton software packages
Bibliographic record
Abstract
ABSTRACT Zero-length spring gravity sensors, such as the Micro-g LaCoste Turnkey Airborne Gravity System (TAGS), are widely used in airborne gravimetry applications due to their virtually drift-free characteristics. However, the quality of the final gravity product depends on the methods chosen to correct for several dynamic effects, including the Eötvös correction, the vertical acceleration correction of the moving aircraft, and the instrument platform tilt correction. For geodetic applications using regional gravity field mapping for geoid determination, the choices of correction method are extremely important because the flights are done uncommonly high and fast. For this reason, and in support of the Gravity for the Redefinition of the American Vertical Datum (GRAV-D) project, the National Oceanic and Atmospheric Administration’s National Geodetic Survey (NGS), and Fugro Geospatial have independently developed airborne gravity processing software packages for TAGS data — called Newton and GravPRO, respectively. We have evaluated the airborne gravity data processing methods that are used in the two software packages. We processed a GRAV-D data set collected by NGS over Louisiana and the Gulf of Mexico in 2008 with both packages, we compared the results for each major data correction applied, and we evaluated unique add-on features and tools. The results determined that the two software packages were very comparable, both yielding an excellent crossover root-mean-square of 1.8 mGal without any crossover adjustment applied. For the worst 14% of survey lines, which had higher levels of aircraft dynamics during flight, GravPRO was able to retain more data due to advantages in its platform tilt correction method. Also, GravPRO’s instrument calibration function, which is based on crossover error analysis, found a 45% improvement in achievable survey precision from approximately 2 mGal to approximately 1 mGal when comparing the test data set to a global gravity model.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".