Developing RADARSAT's METOC Capabilities in Support of Project Polar Epsilon
Bibliographic record
Abstract
The Polar Epsilon project will use Canada's RADARSAT satellites to expand the Canadian Forces' space-based ship and oil spill detection capabilities in the Arctic, Atlantic and Pacific Oceans. RADARSAT's ability to detect ships and oil, however, is influenced by surface winds, waves and currents. As existing sources of meteorological and oceanographic data are too coarse in spatial resolution or too removed in time, this report investigates the feasibility of deriving such information from the RADARSAT imagery itself to conduct a rapid environmental assessment (REA) of(i) minimum detectable ship size and (ii) probability of oil spill false detection. The report also investigates methods to overcome limitations in Canadian Forces' deployed ocean observing infrastructure, which are required to develop and demonstrate spacebased REA products, by using civilian ocean observing systems. In addition, as a means of decreasing limitations inherent in space-based synthetic aperture radar and ocean colour sensors used by Polar Epsilon (i.e. RADARSAT and MODIS), the report identifies and discusses meteorological and oceanographic features of military interest that may be detected by both types of sensors.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".