Herzberg extensible adaptive real-time toolkit (HEART) software architecture
Bibliographic record
Abstract
The National Research Council of Canada Herzberg (NRC-H) is developing the Herzberg Extensible Adaptive Real-time Toolkit (HEART) for the next generation of Adaptive Optics (AO) systems. HEART is a distributed and scalable software framework that includes a collection of libraries, tools and other software that can be assembled to construct a wide variety of real-time AO control systems. HEART is currently under development, implemented in C/C++, and is intended to run on off-the-shelf CPUs; HEART has evolved from the NRC-H design of the Narrow Field Infrared Adaptive Optics System (NFIRAOS) Real Time Controller (RTC) for the Thirty Meter Telescope (TMT), which passed its final design review. HEART is principally designed for Laser Guide Star (LGS) Multi-Conjugate Adaptive Optics (MCAO), however the modular architecture of HEART lends itself to be restructured for different forms of AO systems, such as: Single Conjugate Adaptive Optics (SCAO), Laser Tomography Adaptive Optics (LTAO), Ground Layer Adaptive Optics (GLAO), and Multi-Object Adaptive Optics (MOAO). The primary goal of HEART is to reduce the development time and cost of next generation AO control systems, while providing more reliable and robust control software that minimizes maintenance effort and promotes code reusability.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.011 |
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".