Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it