Statistical approach to systems engineering for the Thirty Meter Telescope
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Core components of systems engineering are the proper understanding of the top level system requirements, their allocation to the subsystems, and then the verification of the system built against these requirements. System performance, ultimately relevant to all three of these components, is inherently a statistical variable, depending on random processes influencing even the otherwise deterministic components of performance, through their input conditions. The paper outlines the Stochastic Framework facilitating both the definition and estimate of system performance in a consistent way. The environmental constraints at the site of the observatory are significant design drivers and can be derived from the Stochastic Framework, as well. The paper explains the control architecture capable of achieving the overall system performance as well as its allocation to subsystems. An accounting for the error and disturbance sources, as well as their dependence on environmental and operational parameters is included. The most current simulations results validating the architecture and providing early verification of the preliminary TMT design are also summarized.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it