Advantages of Right- and Left-looking Radar Imaging Systems for Earth Observation Studies
Bibliographic record
Abstract
Seaming together satellite images has long been important for creating image maps larger than one image frame or strip. Since the swath and direction of orbital tracks are generally not concordant with map co-ordinates, splicing multiple scenes into a common map co-ordinate system requires geo-registration to a co-ordinate grid. Radiometric adjustment and combining or resampling of original pixel values are also often required to achieve a uniformity of tone across the mosaic. In our radar reconstruction of Gondwana (part of the Pangaea supercontinent before its break-up some 200 Ma ago), left-looking RADARSAT-1 data acquired during the Antarctic Mapping Mission and normal mode (right-looking) data were used to create a trans-continental mosaic of part of South America and Africa with a relatively consistent westward look direction. This important aspect of the mosaic would not be possible from a SAR system without left- and right-looking capability. Although the appearance of radar images is strongly affected by incidence angle, heightened awareness about the influence of look direction has upon images may lead to more sagacious use of radar data for Earth observation studies.
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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 | 0.003 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".