Mapping recent lava flows with Radarsat-1 imagery
Bibliographic record
Abstract
The sensitivity of radar imagers towards surface roughness and moisture can be used for mapping rock, soil and vegetation. High-resolution Radarsat-1 data were used to map successive lava flows of the Hekla volcano. The volcano erupted in early 2000. It was monitored throughout the eruptive activity using Radarsat-1 Fine beam images. The Radarsat-1 images were compared with those retrieved from the archive, which had been acquired immediately before the event in the course of Radarsat-1 Background Mission and Disaster Watch Program. Radarsat-1 images were interpreted based on the radar tone and texture as a function of the state of lava solidification, and it was possible to distinguish between lava flows of different stages of the eruptive activity. Using the same tonal and textural the interpretations, an attempt was made to trace the past history of the Hekla volcano. Successive lava flows were delineated and are shown on the image map. The study demonstrates the effectiveness of Radarsat-1 fine resolution imagery for lithological mapping and the usefulness of Radarsat-1 data archives that are being built as a result of the satellite's various baseline acquisitions.
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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 | 0.001 | 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 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".