Gravity disturbance grids from the US GRAV-D airborne surveys
Bibliographic record
Abstract
<!--!introduction!--> Two preliminary gravity disturbance grids are derived from airborne gravity data collected by the National Geodetic Survey, USA as part of the Gravity for the Redefinition of the American Vertical Datum (GRAV-D) project. The data set does not include releases after 2020. Nominal altitude and spatial resolution of GRAV-D surveys are ~6.3 km and ~20 km, respectively. Actual flight altitude varies from ~ 5 km to ~11 km resulting in heterogeneous spatial resolution of the gravity signal from block to block, and within each block. The airborne data used in this study includes all collected flight lines as of December 2022 covering the US territory and the US-Canada Great Lakes region with extension of about 100 km into Canada and Mexico along land borders. The least-squares collocation is chosen for the 3-D interpolation to derive the two grids at a constant height of 6000 m and on the reference ellipsoid, respectively. The two grids are assessed by the Poisson downward continuation for consistence between the two grids; comparisons are made with ultra-high degree Global Gravitational Models (EGM2008, EIGEN6C-4, XGeoidRefA, and XGM2019e), and a regional geoid model is computed by including the terrestrial gravity data. An airborne-only geoid model is determined and evaluated using GPS-Levelling data over the US and satellite altimetry data over the Great Lakes region. The two new gravity grids are expected to contribute to geodetic, geophysical and geological applications, especially development of the North-American and Pacific Geopotential Datum of 2022 (NAPGD2022).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.003 | 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".