Bibliographic record
Abstract
The performance requirements of the Thirty Meter Telescope (TMT) dictate, among others, a thorough understanding of the flow field inside and around the observatory. Mirror and dome seeing as well as dynamic wind loading on the optics, telescope structure and enclosure constitute significant sources of image degradation. A summary of the current status of Computational Fluid Dynamics (CFD) simulations for TMT is presented, with special attention given to the choice of thermal boundary conditions. Detailed simulations of the mirror support assemblies determine the direction of heat flow from important heat sources and provide feedback to the design. They also provide estimates of the heat transfer coefficients for the solid thermal models. A transient radiation model has also been developed for the enclosure and telescope surfaces in order to estimate the heat flux exchange with the air volume. It also provides estimates of the effective emissivity for the solid thermal models. Finally, a complete model of the observatory on a candidate summit is used to calculate air velocity, pressure and temperature for a matrix of given telescope orientations and enclosure configurations. Calculated wind velocity spectra above M1 and around M2 as well as the wind force on the enclosure are used as inputs in the TMT integrated dynamic model. The temperature and flux output of the aforementioned thermal models are used as input surface boundary conditions in the CFD model. Generated records of temperature variations inside the air volume of the optical paths are fed into the TMT thermal seeing model.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".