RADARSAT-1 Background Mission data for flood monitoring
Bibliographic record
Abstract
Demonstrates the value of the global SAR data archives that are being generated as RADARSAT-1 satellite baseline acquisitions called the Background Mission. The RADARSAT-1 Background Mission has now completed two years of SAR data collection by means of multi-mode imaging capabilities. With the help of the wide area ScanSAR beam, a first seasonal snapshot of world continents, continental shelves and polar caps was completed in mid-1997. A second seasonal coverage is in progress over different continents of the world. These seasonal snapshots have furnished valuable reference data that are needed for comparison in the event of unforeseen natural disasters. Bangladesh is a country lying in the delta of the Brahmaputra and the Ganges, which normally flood parts of the country every year following the rainy monsoonal season. However, the monsoonal flooding of 1998 was of historical proportion and covered nearly 2/3rd of the national territory. Monitoring floods of this magnitude is a necessary for planning relief operations, and more importantly, for making long-term flood mitigation strategies. Satellites provide a quick and cost effective way of monitoring floods, though not all satellites are able to deliver timely flood imagery, because of weather conditions that prevail during rainy seasons. RADARSAT is one satellite that is not hampered by weather and day or night conditions. It has the unique capability of furnishing images with variable ground resolution and area covered. Its 500 km-wide ScanSAR swath could capture most of Bangladesh in a single image and thus provide an instantaneous view of the entire flood at a given time. Furthermore, it was possible to reckon the effects of the 1998 flooding by making a comparison with a year of normal flooding.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".