The experimental geoid 2020 – the first joint geoid model for the North American and Pacific Geopotential Datum 
Bibliographic record
Abstract
<p>For the upcoming North American-Pacific Geopotential Datum of 2022, the National Geodetic Survey (NGS), the Canadian Geodetic Survey (CGS) and the National Institute of Statistics and Geography of Mexico (INEGI) computed the first joint experimental gravimetric geoid model (xGEOID) on 1’x1’ grids that covers a region bordered by latitude 0 to 85 degree, longitude 180 to 350 degree east. xGEOID20 models are computed using terrestrial gravity data, the latest satellite gravity model GOCO06S, altimetric gravity data DTU15, and an additional nine airborne gravity blocks of the GRAV-D project, for a total of 63 blocks. In addition, a digital elevation model in a 3” grid was produced by combining MERIT, TanDEM-X, and USGS-NED and used for the topographic/gravimetric reductions. The geoid models computed from the height anomalies (NGS) and from the Helmert-Stokes scheme (CGS) were combined using two different weighting schemes, then evaluated against the independent GPS/leveling data sets. The models perform in a very similar way, and the geoid comparisons with the most accurate Geoid Slope Validation Surveys (GSVS) from 2011, 2014 and 2017 indicate that the relative geoid accuracy could be around 1-2 cm baseline lengths up to 300 km for these GSVS lines in the United States. The xGEOID20 A/B models were selected from the combined models based on the validation results. The geoid accuracies were also estimated using the forward modeling.</p>
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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 | 0.002 | 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".