The impact of DEM errors on the internal error estimate of the geoid
Bibliographic record
Abstract
<!--!introduction!--> The significant contribution of Digital Elevation Models (DEMs) to gravimetric geoid modelling has received a great deal of attention and is well characterized. There exist different (near) global and local DEMs for computing the topographic corrections to satisfy the harmonicity condition of the boundary value problems and to recover the missing gravitational signals in the gap areas of the gravity coverage. Although many studies have focused on the accurate computation of the topographic corrections for both gravity and the geoid, less attention has been given to the final geoid model errors produced by such computations. We intend to partially address this gap by formal error propagation in the process of topographic corrections and estimate their corresponding variances on the geoid heights. The Stokes-Helmert technique is used here and the DEM error model is estimated by independently comparing the existing global DEMs with the height information from ground surveying campaigns. Our initial results show that the error estimate of the topographic corrections in roughed topography (H>4000 m) reaches the centimetre level, confirming the importance of choosing a proper DEM in view of the “1-cm” accurate gravimetric geoid goal.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".