Bibliographic record
Abstract
Thirteen Pan or XS IKONOS Geo-product images over seven study sites with various environments and terrain were tested using different cartographic data and accuracies using a parametric modelling developed at the Canada Centre for Remote Sensing. The objectives were to track the error propagation during the full geometric correction process (bundle adjustment and ortho-rectification). When ground control points (GCPs) are less than 3-m accurate, 20 GCPs over the full image is a good compromise to obtain 3-4 m accuracy in the bundle adjustment. When cartographic co-ordinates are better than 1-m, 10 GCPs are then enough to increase to 2-3 m accuracy with either panchromatic or multiband images. The remaining error is due to GCP definition and plotting. Quantitative and qualitative evaluations of ortho-images were performed with independent check points or digital vector files. Positioning accuracy of 2-4 m is achieved for the ortho-images depending of the elevation accuracy (DEM and grid spacing). To achieve a better final positioning accuracy, such as 1 m, 1-2 m accurate DEM with fine grid spacing is required in addition to well-defined GCPs benchmarked on the ground.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".