GROUND BASED MONITORING MONITORING WITH WITH ADAPTIVE ADAPTIVE OPTICS OPTICS
Bibliographic record
Abstract
Abstract: Since August 1995, near-infrared images of Neptune have regularly been obtained with the University-of-Hawaii adaptive optics system mounted on the f/35 Cassegrain focus of the Canada-France-Hawaii telescope. These images reveal Neptune’s cloud structure with an angular resolution reaching 0.12 ” in the H band. Using adaptive optics (AO), long-term monitoring of Neptune’s cloud activity is now possible from the ground with an angular resolution close to the telescope diffraction limit. Since our first observation of Neptune in August 1995 (Roddier et al. 1997), we have regularly used the Univer-sity-of-Hawaii (UH) AO system and HgCdTe infrared camera to observe Neptune at the f/35 infra-red Cassegrain focus of the CFHT. Let us recall that the UH AO system differs from other AO systems developed for defense applications by its wave-front sensing and compensation technique (Roddier 1988, Roddier et al. 1991). Wave-front sensing is done with an array of photon-counting avalanche photodiodes. Neptune itself is used as a guide source. First operated in 1994, the 13-actuator UH AO system has now been upgraded to 36 actuators. Most of the images presented here were obtained with the new system. It should be noted that a 19-actuator AO system of the same kind has been built by the Canada-France-Hawaii Telescope (CFHT) corporation and has been
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".