The contribution of the GRAV‐D airborne gravity to geoid determination in the Great Lakes region
Bibliographic record
Abstract
Abstract The current official North American Vertical Datum of 1988 (NAVD 88) and the International Great Lakes Datum of 1985 (IGLD 85) will be replaced by a new geoid‐based vertical datum in 2022. The Gravity for the Redefinition of the American Vertical Datum (GRAV‐D) project collects high‐quality airborne gravity data to improve the quality of the gravitational model that underpins the geoid model. This paper validates the contribution of GRAV‐D data in the Great Lakes region. Using the lake surface height measured by satellite altimetry as an independent data set, Global Gravity Models (GGMs) with/without the GRAV‐D data are compared. The comparisons show that the improvement reaches decimeters over Lake Michigan where the historic gravity data have significant errors. Over all lakes, except Lake Erie, the GRAV‐D data improve the accuracy of the gravitational model to 1–3 cm.
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".