On the variation of ice pellet characteristics
Bibliographic record
Abstract
Ice pellets are an important yet little‐studied type of winter precipitation. To better document and understand this type of precipitation, ice pellet particles were imaged with a high‐resolution digital camera during three winter storms in the Montréal area. Particle characteristics, size distributions, precipitation rates, and parameterizations of the particle size distributions were determined from the analysis of the images and compared between events. There were many similarities between the events, and these include the following: The same particle classes were present, the overall size distributions were very similar, and the most common particle diameter (0.6 mm) was the same. The relative lengths of spicules with respect to their base particles were also found to be similar among the three events, with the following relationship being determined: spicule length = 0.45 · particle size. In addition, substantial temporal variability (down to 5 min) was observed in both the particle sizes and the precipitation rate. Also, a range of particle sizes was observed even at low precipitation rates (0.5 mm/h). Differences between the events included the relative numbers of the different classes and the degree of temporal variability. This study confirmed that ice pellet characteristics illustrate many common features in at least three events, although important differences exist as well.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".