RADARSAT-1 mission planning: Meeting customer needs over 5 years of evolving operations
Bibliographic record
Abstract
The RADARSAT-1 Mission Management Office (MMO) has been planning and scheduling the SAR payload, ground reception, and processing facilities that supply the World's synthetic aperture radar (SAR) user community with timely RADARSAT-1 data since 1995. The goal of the MMO is to provide users with timely, high-quality data in a manner continuously being refined to meet changing needs. As the RADARSAT-1 client base and network of stations expanded, the demand for data increased, new applications were found, and new requirements were outlined. Over 5 years the number of ground stations grew from three reception facilities (two in Canada, and one in the United States) to 16 worldwide. To support new facilities, higher data demands, and new data applications, the MMO system required changes to support time-critical information transfer between facilities and improve data delivery timelines and image quality. This paper has two main purposes: describe the current RADARSAT-1 acquisition planning operations system, and summarize the operational improvements made over the last 5 years with the focus being how changes affected SAR users.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".