The MOST Microsatellite Mission: All Systems Go for Launch
Bibliographic record
Abstract
The MOST (Microvariability and Oscillations of STars) astronomy mission under the Canadian Space Agency’s Small Payloads Program is Canada’s first space science microsatellite, and is currently planned for launch in April 2003 aboard a “Rockot ” launch vehicle. The MOST science team will use the 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 incorporates a small (15 cm aperture), high-photometric-precision optical telescope developed by the University of British Columbia and a high performance attitude control system provided by Dynacon Enterprises Limited. As the launch date approaches, the MOST team continues to meet its goals. In addition to the original science objectives, the science team has determined that MOST can also be used to detect light from orbiting exoplanets for the first time. On the engineering side, all of the MOST flight hardware has successfully passed acceptance testing and integration of the spacecraft is underway. This paper summarizes the bonus science mission and discusses the unit and integrated spacecraft testing strategies of the MOST program. It summarizes the plan for spacecraft environmental testing and discusses post-environmental ground and on-orbit operations.
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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 | 0.001 | 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) | 0.127 | 0.080 |
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".