Evaluation of the TanDEM-X Digital Elevation Model by PPP GPS- Analysis and Intermediate Results-
Bibliographic record
Abstract
From mission TerraSAR-X add-on for Digital Elevation Measurements (TanDEM-X) of the German Space Agency (DLR) a global digital elevation model (DEM) will be derived using satellite SAR interferometry. Two radar satellites (TerraSAR-X and Tandem-X) are going to map the earth in such a resolution and accuracy that was not possible in any earlier missions: the aim is an absolute height error of 10m or a relative height error of 2m respectively for 90 % of the data. One method to evaluate the accuracy is the use of kinematic Precise Point Positioning (PPP) GPS measurements. The required accuracy is around 0.5 m. The evaluation of the tracks is carried out using the software GIPSY 5.0 of the Jet Propulsion Laboratory (JPL), USA, as well as using the online service of the Natural Resources of Canada named CSRS-PPP. Both results are combined, thus defining the final solution. After the presentation of results for the first track from Munich, Germany, to Sao Martinho, Portugal in 2009, the intermediate results of five tracks located in Europe, China and South America are described and analyzed in this paper. The final average RMS is calculated to 0.49m and the availability rate is determined to 61%. These values justify the characteristics of the first drive and fulfill the requirements. A second focus of the paper is the analysis of the
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".