Bibliographic record
Abstract
Digital elevation models (DEMs) extracted from high-resolution stereo images (SPOT-5, EROS and IKONOS) using a three-dimensional (3-D) multi-sensor physical model developed at the Canada Centre for Remote Sensing, Natural Resources Canada were evaluated. Firstly, the photogrammetric stereo-bundle adjustment was set-up with few accurate ground control points. DEMs were then generated using an area-based multi-scale image matching method and then compared to 0.2-m accurate lidar elevation data. Elevation linear errors with 68% confidence level (LE68) of 6.5 m, 20 m and 6.4 m were achieved for SPOT, EROS and IKONOS, respectively. The worse results for EROS are mainly due to its asynchronous orbit, which generate large geometric and radiometric differences between the stereo-images. When these differences are not large (such as in the middle of the stereo-pair), 10-m LE68 was achieved. Since SPOT and IKONOS DEMs were in fact a digital terrain surface model where the elevation of land covers (trees, houses) is included, the elevation accuracy is performed depending on the land cover types. LE68 of 1-2 m were obtained for bare surfaces and lakes. However, when compared to sensor resolution, SPOT achieved better results than IKONOS: half-pixel versus 1.5 pixels. On the other hand, LE68 of 4 m to 6.6 m were obtained depending on the forest types (deciduous, conifer, mixed or sparse) and its surface elevation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".