The CGS quasi-geoid based on FFT with degree-banded modification of the Stokes kernel for the Colorado Experiment: ColFFTDB-CGS2019
Bibliographic record
Abstract
The ColFFTDB-CGS2019 gravimetric quasi-geoid model has been computed by the Canadian Geodetic Survey (CGS), Natural Resources Canada. It has been worked out in the frame of the International Association of Geodesy Joint Working Group 2.2.2 "The 1 cm geoid experiment" and the so called "Colorado experiment". The area covered by the model is 110°W ≤ longitude ≤ 102°W, 35°N ≤ latitude ≤ 40°N with a grid spacing of 1' in both latitude and in longitude. The solution is based on the Stokes-Helmert method, which is used with the degree-banded Stokes kernel modification (Huang and Véronneau, 2013). A band-wise spectral scheme is developed to combine the global model, airborne and surface gravity data, contributing to low-, medium- and high-degree bands of the geoid, respectively. In particular, the global gravity model provides entirely the low-degree band up to degree 150 from where it goes into transition with the surface gravity data up to degree 210. The surface gravity data complete the geoid frequencies up to degree 10800 with complement information from SRTM 3″ digital terrain model. As for the GRAV-D airborne gravity data, they provide corrective values directly to the geoid model, contributing between degrees 210 and 790 with smooth transition at each end. The geoid computation is performed by Fast Fourier Transform (FFT) numerical integration and then the quasi-geoid model is obtained by adding a classic geoid to quasi-geoid correction grid. The accuracy of the quasi-geoid model, when compared against GSVS17 GPS/leveling, is equal to 1.8 cm. The geoid model is provided in ISG format 2.0 (ISG Format Specifications), while the file in its original data format is available at the model ISG webpage.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".