Systems budgets architecture and development for the Maunakea Spectroscopic Explorer
Bibliographic record
Abstract
The Maunakea Spectroscopic Explorer (MSE) project is an enterprise to upgrade the existing Canada-France- Hawaii observatory into a spectroscopic facility based on a 10 meter-class telescope. As such, the project relies on engineering requirements not limited only to its instruments (the low, medium and high resolution spectrographs) but for the whole observatory. The science requirements, the operations concept, the project management and the applicable regulations are the basis from which these requirements are initially derived, yet they do not form hierarchies as each may serve several purposes, that is, pertain to several budgets. Completeness and consistency are hence the main systems engineering challenges for such a large project as MSE. Special attention is devoted to ensuring the traceability of requirements via parametric models, derivation documents, simulations, and finally maintaining KAOS diagrams and a database under IBM Rational DOORS linking them together. This paper will present the architecture of the main budgets under development and the associated processes, expand to highlight those that are interrelated and how the system, as a whole, is then optimized by modelling and analysis of the pertinent system parameters.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".