Bibliographic record
Abstract
The present study is concerned with an investigation of the potential of third generation satellite-borne synthetic aperture radar (SAR) imagery, as exemplified by that flown onboard the Canadian Space Vehicle Radarsat, for topographic mapping applications at medium and small scales (1/50000-1/250000). Two test areas in Saudi Arabia and Sudan were made to undergo a series of extensive and comprehensive geometric accuracy tests of the Radarsat system to investigate the ability of these images for topographic mapping applications, DEM generation, radar ortho-photo production and radar profiling. The results show that the SAR fine mode F2 image of Riyadh is suitable for mapping at scale of 1/50000, while the standard mode S1 image satisfies the requirements of planimetric mapping at 1/100000 scale. The wide mode W2 image of Kassala area in Sudan is commensurate with only the requirements of 1/400000 scale and smaller. In this respect, the effects of uncompensated geometric scale errors in their raw uncorrected state are clearly noticeable in most images. Digital elevation model generation is possible with high resolution SAR stereo techniques. In this respect the accuracy achieved in
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".