Software life cycles in astronomy: 40 years of computing at CFHT
Bibliographic record
Abstract
The Canada-France-Hawai‘i Telescope, operational since 1979, currently has five scientific instruments ranging from a few years old to decades old, making it highly productive today. At this world-class facility, computing systems were built and software was developed to support some of the first and largest mosaic CCD cameras, control the telescope, transition from classical observing to queue scheduled observing, and to allow it to be remotely controlled. This involved many choices of computing platforms, programming languages, and significant open-source software development. Software tools and computing infrastructure have been continually adapted, purchased, made in house, and maintained. These “life cycles” are not easy to predict at their start. A retrospective analysis of how these have played out for over 40 years can inform future projects at CFHT and in astronomy in general. We detail the major decision points and speculate how outcomes would have been different had we taken alternative paths. We discuss a rationale for making software choices in future projects.
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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 | 0.056 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".