Polar Epsilon MODIS and Fused MODIS / RADARSAT MetOc Products for National Defence and Domestic Security
Bibliographic record
Abstract
Abstract : In preparation for Polar Epsilon's MODIS and RADARSAT satellite reception systems, this project investigates meteorology and oceanography (MetOc) products derived from MODIS data for defence, search and rescue and environmental security operations. It also investigates whether operational improvements can be gained through production of fused MODIS/RADARSAT products, but in both cases the emphasis is on detection of oceanographic parameters and features. This assessment is based on interaction with representatives of federal operational MetOc, search and rescue, ice and oil spill monitoring centres and synthesis of unclassified literature as a means to obtain (i) an up-to-date presentation of Canada's maritime rapid environmental assessment requirements and (ii) MODIS R&D recommendations for DRDC Ottawa. This information is used to develop a Canadian Forces strategy for MODIS product development, including identification of certain critical operational linkages. DRDC Ottawa's emerging RADARSAT fine-scale wind product is identified as a critical component of this strategy as is the need for a co-ordinated defence, search and rescue and environmental security operational MetOc data access and distribution system.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".