HEART: Gemini Infrared Multi-Object Spectrograph (GIRMOS) Real-time Controller using Herzberg Extensible Adaptive Real-time Toolkit (HEART)
Bibliographic record
Abstract
This paper will discuss the Gemini Infrared Multi-Object Spectrograph (GIRMOS) with a focus on the design of its facility class Adaptive Optics (AO) Real Time Controller (RTC). The GIRMOS Adaptive Optics Real-Time Controller (GIRMOS RTC) will be developed using the Herzberg Extensible Adaptive Real-time Toolkit (HEART), a C/C++ software framework for constructing RTCs that targets general-purpose CPUs and standard networking hardware. The GIRMOS RTC just finished a successful pre-build phase where the custom parts of GIRMOS were designed and it was shown how the design incorporated HEART’s software modules. The GIRMOS RTC as a Multi-Object implementation of HEART will leverage a decade of design, modelling, and prototyping effort aimed to support the performance and configurability requirements of AO systems, with support for multiple client science instruments. This paper will discuss how HEART can be customized for a Multi-Object AO (MOAO) system.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".