Evaluating ALOS-PALSAR for Ice Monitoring - What Can L-band do for the North American Ice Service?
Bibliographic record
Abstract
The Canadian Ice Service (CIS), the U. S. National Ice Center (NIC), and the International Ice Patrol (IIP), partners in the North American Ice Service (NAIS), have individually and jointly used airborne and spaceborne synthetic aperture radar data extensively for almost three decades in their daily ice monitoring operations. SAR's unique ability to penetrate clouds and weather make these data invaluable to the NAIS' efficient environmental stewardship and safe operation in Canadian and U. S. waters. Since 1992, solely C-Band satellite radar has been in use as operational SAR missions such as ERS 1 & 2, RADARSAT-1, and Envisat ASAR have selected it as the band of choice. With the launch of RADARSAT-2 on December 2007 and approved plans for Sentinel-1, the NAIS intends to continue utilizing C-Band data in its daily operations. However, it is important to understand the unique and complementary capabilities of other SAR bands. The January 2006 launch of the JAXA ALOS satellite and present availability of L-band SAR data from its PALSAR instrument provides a unique opportunity to assess L-band data for application to ice monitoring. ALOS/PALSAR availability also provides the potential for examining the synergies between L-Band data and C-Band data available from the current and planned C-Band missions. The existing literature suggests that the use of different frequencies could be advantageous in certain ice conditions, which is of interest to the NAIS because of the vastness of the geographical area monitored annually and the associated variations in ice regimes and conditions.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".