The MOST Microsatellite Mission: Canada’s First Space Telescope
Bibliographic record
Abstract
Abstract. The MOST (Microvariability and Oscillations of STars) astronomy mission has been chosen by the Canadian Space Agency’s Small Payloads Program to be Canada’s first space science microsatellite, and is currently planned for launch in late 2001. The MOST science team will use the MOST satellite to conduct long-duration stellar photometry observations in space. A major science goal is to set a lower limit on the age of several nearby “metal-poor sub-dwarf” stars, which may in turn allow a lower limit to be set on the age of the Universe. To make these measurements, MOST will incorporate a small (15 cm aperture), high-photometric-precision optical telescope to be developed by UBC. The MOST bus and ground stations are being developed by Dynacon and the University of Toronto, in collaboration with AMSAT Canada. Several of the bus subsystems are based on similar designs that have been flown on past AMSAT microsatellites. However, the MOST attitude control system is unusual for a microsatellite, requiring highlyaccurate (< 30 arc-seconds) three-axis inertially-fixed stabilization, far better than can be achieved using the gravitygradient boom stabilization approach typical of many past microsatellites. Dynacon will provide the MOST ACS, based on its Miniature Reaction Wheel (MRW) and High Performance Attitude Control (HPAC) products. MOST’s HPAC capability will enable it to be one of the first operational space science microsatellites.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".