Block Adjustement Of Landsat-7 Etm
Bibliographic record
Abstract
This research study shows the potential of large image-block adjustment with nadir viewing sensor images. The method uses the geometric correction system developed for multi-source images at the Canada Centre for Remote Sensing. The results with 15 Landsat-7 ETM images show that the same accuracy can be obtained with a large image block than with a single image using the same number of ground control points (GCPs). The number of GCPs depends on cartographic data accuracy to reduce the propagation of GCP error in the leastsquare block adjustment. To insure consistency and convergence in the block adjustment, strips of same-path and date images has to be generated. Furthermore, elevation tie points (with known elevation value) are used in the overlaps (North-South and East-West) because the viewing-angle differences of overlapping images are small: less than 1 in North-South overlaps and less than 10 in East-West overlaps.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".