Bibliographic record
Abstract
<strong class="journal-contentHeaderColor">Abstract.</strong> We explored the improvement in starphotometry accuracy using a multi-star Langley calibration in lieu of the more traditional one-star Langley approach. Our goal was a 0.01 calibration-constant repeatability accuracy, at an operational sea-level facility such as our Arctic site at Eureka. Multi-star calibration errors were systematically smaller than single star errors and, in mid-spectrum, approached the 0.01 target for an observing period of 2.5 h. Filtering out coarse mode (super <em>µ</em>m) contributions appears mandatory for improvements. Spectral vignetting, likely linked to significant UV/blue spectrum errors at large airmass, may be due to limiting field-of-view and/or sub-optimal telescope collimation. Starphotometer measurements acquired by instruments that have been designed to overcome such effects may improve future star magnitude catalogues and consequently starphotometry accuracy.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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; both teacher heads agree on what is shown here.
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".