OPA! The Original PolyOculus Array: a status update
Bibliographic record
Abstract
The PolyOculus technology, developed by CREOL’s Astrophonics group, creates a large-area-equivalent telescope using fiber optics and a photonic lantern to link several semi-autonomous, small, inexpensive, commercial-off-theshelf telescopes. The Original PolyOculus Array, OPA, will use seven, Celestron 11” telescopes with iOptron centralbalanced equatorial mounts (CEM 70) to create a ~0.75m equivalent optical telescope for spectroscopic follow up observations of astronomical events. This telescope array will include 7 acquisition and guiding systems (one per telescope) to appropriately center and finely focus objects in the telescopes’ field of view along with an atmospheric dispersion corrector for each unit. That light will then be sent through single, multimode, optical fibers (one fiber per telescope) and to a photonic lantern where the light from all seven telescopes will be combined then sent to a spectrograph. The photonic lantern has demonstrated over 91% efficiency in combined optical light. The Original PolyOculus Array will be commissioned and operated at Mount Laguna Observatory in southern California. OPA will be the prototype to an eventual, more numerous PolyOculus driven array and other future PolyOculus arrays with different applications.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.042 | 0.035 |
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".