Mauna Kea Sky Transparency from CFHT SkyProbe Data
Bibliographic record
Abstract
Nighttime sky transparency statistics on Mauna Kea are reported based on data from the Canada-France-Hawaii Telescope SkyProbe monitor. We focus on the period beginning with the start of MegaCam wide-field optical imager operations in 2003, and continuing for almost three years. Skies were clear enough to observe on 76% of those nights; attenuations were less than 0.2 magnitudes up to 60% of the time. An empirical model of cloud attenuation and duration is presented allowing us to further characterize the photometric conditions. This is a good fit tothe SkyProbe data, and indicates that Mauna Kea skies are truly photometric (without cloud) an average of 56% of the time, with moderate seasonal variation. Continuous monitoring of transparency during the night is necessary to overcome fluctuations in attenuation due to thin cloud.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".