The Thirty-Meter Telescope project design and development phase
Bibliographic record
Abstract
The U.S. National Observatories have responded to the call of the astronomy decadal survey committee to develop a Giant Segmented Mirror Telescope by forming the AURA New Initiatives Office. Drawing on the engineering and scientific staffs of the National Optical Astronomy Observatory and the Gemini Observatory, NIO has for the past 30 months carried out studies aimed at: understanding the key science drivers for a thirty-meter telescope; developing a feasible point design that is responsive to the science goals; and identifying key technical issues that must be solved in order to successfully build such a telescope. In parallel, NIO has followed the charge of the decadal survey to identify potential private and international partners to fulfill the committee vision of a public-private partnership to build and operate this facility. NIO has now joined with two other groups -- the CELT Development Corporation (a partnership between the University of California and the California Institute of Technology) and the Association of Canadian Unviersities for Research In Astronomy (ACURA) -- to initiate the next step, the design & development (D & D) phase of a joint project that is being called the Thirty-Meter Telescope (TMT) Project. This paper reviews the plans for the TMT D & D phase, including the organizational structure, science requirements, and plans for conceptual design studies, technology development, and site selection.
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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 | 0.029 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".