Developing a Role for Small Satellites in the Canadian Forces
Bibliographic record
Abstract
Space-based capabilities are playing an ever-increasing role in support of military operations. The issue faced by many nations is evaluating how space-based capabilities best suit their own needs, and how to acquire these capabilities, either by indigenous development within the nation's resources, or through international leveraging with partners and allies. Traditionally, many nations such as Canada have not had the resources to invest in indigenous military satellites. In recent years, however, satellite technology has become more affordable, space-based capabilities are now within reach to an increasing number of military organizations -- the challenge is to determine what space-based applications make sense to invest in. Just as important is also creating a positive environment such that new space-based proposals are accepted. This can be a significant obstacle as many have become apprehensive towards satellite programs when faced with the choice of funding space-based initiatives against more familiar technologies. Defence Research and Development Canada (DRDC) is addressing these issues, defining the role of space-based capabilities, in concert with other terrestrial capabilities, to provide the Canadian Forces with an appropriate, effective suite of technologies that best meets national and deployed operational needs. This paper will outline DRDC's efforts towards developing a sustainable small satellite program. Current research and development initiatives will be presented including two microsatellite demonstration missions currently underway. The benefit of partnership with the Canadian Space Agency (CSA) and other Allied programs will be reviewed along with the future possibilities of leveraging NATO collaborations. Finally, a discussion will be presented outlining a strategy to best influence positive change and acceptance within the Canadian Forces to adopt space-based technologies as a routine capability generator.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".