Cost-effective approach to quality assurance via failure modes and effects analysis for the development of GIRMOS for the Gemini North Telescope
Bibliographic record
Abstract
The Gemini Infra-Red Multi-Object Spectrograph (GIRMOS) is a high-resolution integral-field spectroscope and imager being built by a consortium of Canadian universities and institutions, along with the International Gemini Observatory (Gemini) and the Korea Astronomy and Space Science Institute (KASI). The team needed a cost-effective way to bring a degree in Product and Quality Assurance to bear on instrument development, but without availability of a dedicated team. Advice and support from the Thirty Meter Telescope (TMT) Systems Engineering Team enabled GIRMOS to tailor and scale the TMT approach to fit within the available resources of a much smaller project. This Failure Modes and Effects Analysis (FMEA) method more easily allowed geographically distributed subsystem teams to work independently within an agreed-upon FMEA framework that rolled up into a System-level analysis. The TMT FMEA framework reduced the effort involved in all the follow-on work that used the same data set, namely sparing analysis, reliability and uptime analyses, and accelerated life testing.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".