Stereo Radargrammetry With Radarsat-2 in the Canadian Arctic
Bibliographic record
Abstract
A digital elevation model (DEM) is generated using a Radarsat-2 (R-2) high-resolution stereo pair acquired over challenging icefields and fjords site in the Canadian Arctic (80$\%$of ice-covered areas and almost 50$\%$of$>40^{\circ}$slopes). The stereo DEM was produced using a new hybrid radargrammetric model, which did not need any reference cartographic data. The accuracy of the stereo DEM was quantified with a topographic 1959 DEM and Ice, Cloud, and land Elevation Satellite (ICESat) data, over and outside the icefields and as a function of slope. The method could be applied to ice-covered areas with 1-sigma 25- or 18-m accuracy over less than 30$^{\circ}$or 5$^{\circ}$slopes, respectively. In addition, a systematic elevation lowering of around 10 m computed between 1959 DEM and recent elevation data (R-2 and ICESat) could be due to icefield wastage over the last 50 years.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".