Detailed observations of ice pellets and an analysis of their characteristics and formation mechanisms
Bibliographic record
Abstract
Winter storms affect all Canadians and many of their impacts are associated with precipitation. This precipitation can occur as rain, snow, freezing rain or ice pellets. Some research has been conducted on all of these types of precipitation but the least attention has been paid to ice pellets. The atmospheric environment conducive to ice pellets is known in general but the detailed nature of the actual particles is not. To begin to address this issue, a high resolution digital camera was used to photograph ice pellets for 4 hours during a winter storm at Mirabel, Quebec in November 2003. A total of 1023 images were analyzed to determine the morphology, shapes, and size distributions of the ice pellets. Some ice pellets were opaque, others were clear, and some had bands of varying opacity. At most, 7% of the particles were spherical. Many particles exhibited bulges, fractures, and spicules. The occurrence of such features suggests that much or all of the initial freezing occurred on the surface as opposed to the drop interior. Approximately 9% of the particles observed were aggregates of 2-5 smaller particles. The ice pellets ranged up to 5 mm in diameter for aggregate particles and up to 3 mm in diameter for individual particles. The average diameter of all particles was 1 mm. A range of different particle characteristics were observed to be occurring simultaneously throughout the storm. Collectively, such observations as well as process model results imply that different freezing mechanisms were occurring simultaneously, and that collisions between semi-frozen ice pellets must have been occurring to produce aggregates.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".