Preparing for operational use of RADARSAT-2 data at the Canadian Ice Service
Bibliographic record
Abstract
As one of the world's largest operational users of RADARSAT-1 data, the Canadian Ice Service (CIS) is looking forward to the future launch of RADARSAT-2. The RADARSAT-2 mission, including both the Space and Ground segments, promises several technical enhancements beyond RADARSAT-1, which should be beneficial to the CIS. Towards that end and to prepare for operational use of the data, the CIS has been working closely with the Canadian Space Agency (CSA) and MacDonald Detwiller (MDA), particularly on the Ground Segment, to ensure that the RADARSAT-2 system will best meet our operational needs. Additionally, the CIS is investigating the operational utility to be gained from some of the advanced capabilities of the SAR sensor, specifically the selective single-, dual-, and quad-polarization modes. In this paper we briefly examine, from an operational perspective, various elements and enhancements of the RADARSAT-2 Space and Ground segments, and issues concerning data usage to be addressed in order to maximize the operational utility of RADARSAT-2 data at the CIS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".