Remote Predictive Mapping of the Tunnunik Impact Structure in the Canadian Arctic using Multispectral and Polarimetric SAR Data Fusion
Bibliographic record
Abstract
The 28-km diameter Tunnunik impact structure in northern Victoria Island, Arctic Canada, was mapped using ASTER, Landsat 8, RADARSAT-2 polarimetric synthetic aperture radar (SAR), and Quickbird data. Multispectral analysis was accomplished through band ratios, MNF transform, and spectral matching algorithms, from which 4 distinct spectral units were defined. Polarimetric SAR decompositions also showed different scattering mechanisms for these 4 units indicating different surface roughness properties. These multispectral and polarimetric SAR observations were combined with detailed surface textures and morphological features as visible in very high-resolution Quickbird imagery (61 cm/pixel). Remote sensing parameters and their thresholds for characterizing each unit were implemented into a decision-tree algorithm and a remote predictive geological map was produced. Subsequent field and follow-up laboratory investigations enabled the ground-truthing of these predictions. The geological units were defined as follows: (i) (smooth) fluvioglacial deposits, (ii) (moderately rough) chert-bearing dolostone, (iii) (rough) dolostone, and (iv) (rough) dolostone covered by silicified surfaces. The rough surfaces characterized by multiple scattering in the polarimetric SAR decomposition correspond to the occurrences of weathered carbonate rocks, which are relatively resistant to weathering and form blocky surfaces. This shows that SAR-derived surface roughness properties can greatly contribute to defining geological units by combining with lithological mapping.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".