Bibliographic record
Abstract
The quality of astronomical images obtained with the 3 m liquid‐mirror telescope (LMT) of the NASA Orbital Debris Observatory (NODO) and with the University of British Columbia 6 m Large Zenith Telescope (LZT) is assessed and compared to that of conventional instruments. Analysis of star images in long‐exposure drift‐scan data indicates that the profile of the image core is primarily set by atmospheric turbulence. Defocused star images reveal the presence of low‐amplitude waves on the surface of the mercury, also seen in laboratory tests. The effect of these waves is to diffract light into the wings of the point‐spread function. Analysis of the intensity profiles of stellar images can therefore probe the structure of the mirror surface on scales smaller than the atmospheric coherence length, which is about an order of magnitude larger that the characteristic wavelengths of the surface waves. It is found that the rms surface height error produced by these waves was approximately 37 nm for the NODO LMT. Improvements to the rotational speed stability of liquid mirrors, reduction of the thickness of the mercury layer, and use of a protective Mylar cover have allowed the LZT to reduce this source of error to approximately 9 nm rms, thereby achieving an image quality approaching that of conventional telescopes.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".