NFIRAOS in 2015: engineering for future integration of complex subsystems
Bibliographic record
Abstract
The Narrow Field InfraRed Adaptive Optics System (NFIRAOS) will be the first-light facility Adaptive Optics (AO) system for the Thirty Meter Telescope (TMT). NFIRAOS will be able to host three science instruments that can take advantage of this high performance system. NRC Herzberg is leading the design effort for this critical TMT subsystem. As part of the final design phase of NFIRAOS, we have identified multiple subsystems to be sub-contracted to Canadian industry. The scope of work for each subcontract is guided by the NFIRAOS Work Breakdown Structure (WBS) and is divided into two phases: the completion of the final design and the fabrication, assembly and delivery of the final product. Integration of the subsystems at NRC will require a detailed understanding of the interfaces between the subsystems, and this work has begun by defining the interface physical characteristics, stability, local coordinate systems, and alignment features. In order to maintain our stringent performance requirements, the interface parameters for each subsystem are captured in multiple performance budgets, which allow a bottom-up error estimate. In this paper we discuss our approach for defining the interfaces in a consistent manner and present an example error budget that is influenced by multiple subsystems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| 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".