Accuracy of Point and Line Measures of Boundary Layer Cloud Amount
Why this work is in the frame
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Bibliographic record
Abstract
Many authors have used upward-looking instruments, such as a laser ceilometer, to estimate the cover of fairweather cumuli, but little has been mentioned as to the accuracy of these measurements. Results are presented, using a simulated cloud field and a virtual aircraft, that show that sampling errors can be very large for averaging times commonly used with surface instruments. A set of empirical equations is found to provide an estimate of the errors associated with averaging time and earth cover. These relationships can be used to design observation strategies (averaging time or flight-leg length) that provide earth-cover estimates within desired error bounds. These results are used to guide a comparison between earth cover measured by an airborne upward-looking pyranometer and earth cover observed by airborne scientists in a research aircraft. In general, the agreement between these two methods is good.
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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 | 0.000 |
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 it