Step Towards the Development of Lunar Liquid Mirror Telescope
Bibliographic record
Abstract
ABSTRACT: Desolate, airless and with no people around for hundreds of thousands of kilometers, the Moon is a great place for astronomers. Sky watchers have an enduring hope of one day building a lunar observatory, where gleaming from the earliest stars can be snared without the curse of man-made light pollution and Earth's atmospheric distortion. But making telescopic mirrors is eye-wateringly expensive, for it requires grinding and polishing glass to an accuracy of a few tens of billionths of a meter and after making a mirror, there's the risk of breaking it when you haul it to the Moon. So, the scientists brought the idea to use a liquid mirror telescope on the surface of the moon, to be known as the lunar liquid mirror telescope (LLMT), that could be hundreds of times more sensitive than the Hubble Space Telescope. The potential of a return of human presence to the Moon raises the possibility of significant lunar infrastructure and with it the possibility of astronomical installations which can make use of the lunar surface as a stable platform and take advantage of the lack of atmosphere. A study is being conducted to determine the feasibility of constructing a lunar liquid mirror telescope, or LLMT, by NASA Institute for Advanced Concepts (NIAC) and the Canadian Space Agency.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".