Left- and Right-Looking RADARSAT-2 Data for Mosaics of Ancient Supercontinents
Bibliographic record
Abstract
The Pangaea supercontinent began its break-up some 200 Ma ago, during which time the ancient cratons of West Africa and San Francisco-Congo of Gondwana were rifted apart. These older parts of the continental crust now reside in the African and South American Shields, but share a common geological past. Geological maps of reconstructed Pangaea aid geologists to understand the tectonic history of the evolving Earth, the global distribution of rock units and ore deposits. It follows that radar and other remotely sensed images of Earth can be mosaicked in the same fashion to provide supplementary information in support of such investigation. In our radar mosaic for part of Gondwana, left-looking RADARSAT-1 data of west Africa acquired on ascending passes during the Antarctic Mapping Mission and normal mode (right-looking) data of South America from descending passes were first seamed together separately. The two continental image maps were then rotated into their pre-break-up configuration to create a radar mosaic with a relatively consistent westward radar look. This critical aspect of the mosaic would not be possible from a SAR system without left- and right-looking capability. A consistent look direction is of great importance when landform interpretations are made. The left and right pointing of the RADARSAT-2 antenna will enable routine data collection of this kind for similar 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".