IMAKA: imaging from MAuna KeA optical design
Bibliographic record
Abstract
The 'IMAKA (Imaging from MAuna KeA) instrument is a wide field visible light imager incorporating Ground Layer Adaptive Optics (GLAO) to take maximum advantage of the excellent seeing available at the Canada-France-Hawaii Telescope (CFHT). It requires better than 0.3" image quality simultaneously over a total field of view of approximately one square degree (~3 x 10-4 sr). This requirement along with other criterions and constraints raises a challenge for optical design. The advent of orthogonal transfer (OT) CCDs allows the tip-tilt portion of the atmospheric correction to be performed at the science detector itself. 'IMAKA will take full advantage of the large array mosaics of OTCCDs. Since the size of the adaptive mirror would drive the cost and hence implementation of the overall 'IMAKA instrument, a review of possible optical design configurations which minimize the size (diameter) of the deformable mirror is undertaken. A promising design was obtained and developed in more detail. This all reflective system is described along with its predicted optical performance. An opto-mechanical design concept was developed around this nominal optical design which takes into account various constraints due to its required location on the top end of the Canada France Hawaii Telescope. The design concept is feasible and meets the optical performance requirements.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.010 |
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".