Bibliographic record
Abstract
Abstract Ice crystals in clouds in the atmosphere have shapes that relate to their density, terminal fall velocity, growth rate and radiative properties. In calculations for climate‐change predictions, forecasting of precipitation, and remote‐sensing retrievals, idealized crystal shapes such as columns, needles, plates and dendrites are often assumed. The objective of this work is to study the frequency of occurrence of different habits of ice particles in natural clouds from aircraft observations. Images of cloud particles were measured by a PMS Optical Array Probe‐2DC at 25 μm resolution installed on the National Research Council Convair‐580. The processing of particle images was conducted with a newly developed algorithm for pattern recognition. Data were collected during four field projects in the Canadian and US Arctic over the North Atlantic near Newfoundland, and over the Great Lakes. Approximately 5 × 106 images of cloud particles having a size larger than 125 μm were analysed. The cloud particles were classified into four categories; spheres, irregulars, needles/columns and dendrites. The habit classification of particles was done for three different size ranges: > 125 μm, >250 μm. and >500 μm. The frequency of occurrence of different habits was found for each 5 degC temperature interval in the range −45°C < T < 0°C. It was concluded that the majority of ice particles in natural clouds were of irregular shape. The frequency of occurrence of irregular ice decreases with increasing particle size. On average, the concentration of particles larger than 125 μm was approximately constant down to −35°C, whereas the concentration of particles larger than 500 μm decreased at temperatures below −15°C. Since the data were collected in different climatic zones within many cloud lypes. and covered a significant cloud path length (3.6 × 104 km), the conclusions are applicable to most stratiform clouds containing ice.
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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 | 0.000 |
| 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) | 0.000 | 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 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".