Freezing Fog and Drizzle Observations
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">Fog and drizzle observations collected during the arctic weather and SAR (Search and Rescue) operations (SAAWSO) project at sub-freezing temperatures (T) are analyzed in this study to identify icing conditions, improve ground-based in-situ and remote sensing observations, and develop icing parameterizations for numerical weather prediction (NWP) models. The SAAWSO project took place during the 2012-2013 winter conditions that occurred over St. John's, NFL, Canada. Observations were obtained by a Droplet Measuring Technologies Fog Measuring Device (FMD), a ground cloud imaging probe (GCIP), a Radiometrics Profiling Microwave Radiometer (PMWR), a Rosemount icing detector, a laser disdrometer, and surface meteorological sensors. Precipitation, wind, and radiation data were also collected. Results suggest that observations obtained from integrated in-situ and remote sensors can be used to characterize icing conditions.</div></div>
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".