Bibliographic record
Abstract
Two stereo pairs generated with standard mode images (S1-S7 and S4-S7) and one with fine mode images (F1-F5) are used to evaluate the potential of RADARSAT-SAR for extracting planimetric features, such as roads on a PC-based stereo workstation. First, monoscopic and stereoscopic plotting for the GCPs are performed to evaluate the impact on the accuracy. It is prerequisite, especially for smaller intersection angle stereo pairs, to acquire GCPs in stereoscopy since monoscopic collection mode degrades the relative and absolute orientations of the stereo model with a ratio of two to four depending of the stereo geometry. The roads are then interactively stereo extracted by an operator and compared with the roads of the digital topographic maps. Statistical results over a large sample (more than 900 km) show accuracy of about 15-24 m for fine mode and 25-50 m for standard mode stereo-pairs with 90% confidence levels, independently of the stereo configuration. The impact of image sampling on the road positioning accuracy is also addressed. Finally, comparisons with the ortho-rectification process show that the stereoscopic method to extract planimetric features is slightly more accurate.
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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 | 0.003 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".