RADARSAT-1 Image Quality - Continuing Success in Extended Mission
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
RADARSAT-1, the first Canadian SAR remote sensing satellite, was launched on November 4, 1995. After commissioning, it was put in to routine operations on April 1, 1996. Since then, it has been operating successfully, even after completing its five and a quarter years of design lifetime, and providing data to users for their intended applications. Significant effort continues to be expended in the provision of high quality products to users generated by the Canadian Data Processing Facility (CDPF). After initial calibration, both single beams and ScanSAR are monitored routinely as part of the Maintenance Phase for image quality performance. Image quality is monitored through periodic measurements of impulse response function, location error and radiometry, using images of the Amazon Rainforest and RADARSAT-1 Precision Transponders (RPTs). ScanSAR radiometry is also monitored through periodic measurements of the Amazon Rainforest. A major upgrade of the ScanSAR processor completed recently in CDPF made significant improvements in image quality and radiometry. Measured results indicate that image quality is better than system specification and maintained. This paper will describe the overall process of data acquisition, data analysis and recalibration for image quality maintenance. 1.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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 it