Risk Reduction Activities for the Near-Earth Object Surveillance Satellite Project
Bibliographic record
Abstract
The Near-Earth Object Surveillance Satellite (NEOSSat) is a joint project between Defence Research and Development Canada (DRDC) and the Canadian Space Agency (CSA). The NEOSSat project is developing the Canadian multi-mission micro-satellite bus to satisfy two concurrent missions: detecting and tracking of near-Earth asteroids (Near Earth Space Surveillance: the NESS mission) and obtaining metric data on deep-space satellites (High Earth Orbit Surveillance System: the HEOSS mission). To ensure both science teams can employ the NEOSSat spacecraft to its full potential, a Mission Planning System (MPS) will be developed to automate the scheduling of both the HEOSS and NESS observations. As a first risk reduction activity for the NEOSSat project, a prototype of the MPS software has been developed to help in the definition of the system requirements as well as to identify and reduce the risks associated with the development of this software system. In a second risk-reduction effort, a space-based satellite tracking experiment was conducted using the MOST (Microvariability Oscillations of STars) microsatellite. Good quality metric tracking data were obtained and the satellite brightness was estimated. This paper discusses the NEOSSat project, the MPS prototype, and the MOST satellite tracking experiment and results.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".