Experiments at the W.M. Keck Observatory to support the Thirty Meter Telescope design work
Bibliographic record
Abstract
In order to validate various assumptions about the operating environment of the Thirty Meter Telescope (TMT), to validate the modeling packages being used to guide the design work for the TMT and to directly investigate the expected operation of several subsystems we have embarked on an extensive campaign of environmental measurements at the Keck telescopes. We have measured and characterized the vibration environment around the observatory floor and at certain locations on the telescope over a range of operating conditions. Similarly the acoustic environment around the telescope and primary mirror has been characterized for frequencies above 2 Hz. The internal and external wind and temperature fields are being measured using combined sonic anemometer and PRT sensors. We are measuring the telescope position error and drive torque signals in order to investigate the wind induced telescope motions. A scintillometer mounted on the telescope is measuring the optical turbulence inside the telescope tube. This experimental work is supplemented by an extensive analysis of telescope and engineering sensor log files and measurements, primarily those of accelerometers located on the main telescope optics, primary mirror segment edge sensor error signals (residuals), telescope structure temperature measurements and the telescope status information.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".