Visibility during Blowing Snow Events over Arctic Sea Ice
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract A field study on visibility during Arctic blowing snow events over sea ice in Franklin Bay, Northwest Territories, Canada, was carried out from mid-January to early April 2004 during the Canadian Arctic Shelf Exchange Study (CASES) 2003–04 expedition. Visibilities at two heights, wind and temperature profiles, plus blowing and drifting snow particle flux at several heights were monitored continually during the study period. Good relations between visibility and wind speed were found in individual events of ground blowing snow with coefficients of determination >0.9. Regression equations relating 1.5-m height visibility to 10-m wind speed can be used for predicting visibility with a mean relative error in the range of 19%–32%. Similar regression functions obtained from the data for observed visibility of less than 1 km could predict visibilities more accurately for more extreme visibility reductions and wind speeds (>9.5 m s−1) with mean relative error ranging from 15% to 26%. For the event of ground blowing snow, a simple power law relationship between wind speed and visibility is sufficient for operational purposes. A poorer relationship was observed in the event of blowing snow with concurrent precipitating snow. A theoretical visibility model developed by Pomeroy and Male fit well with observed visibilities if using a mean radius of 50 μm and an alpha value of 10. The predicted visibility had a mean relative error of 30.5% and root-mean-square error of 1.3 km. The observed visibility at 1.5 m had a strong relation with particle counter readings, with an R2 of 0.92, and was consistent among all events.
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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 | 0.001 | 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) | 0.001 | 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