Improved Imaging and Image Analysis System for Application to Measurement of Small Ice Crystals
Bibliographic record
Abstract
Abstract Accurate knowledge of ice particle size and shape distribution is required for understanding of atmospheric microphysical processes. While larger ice particles are easily measured with a variety of sensors, the measurement of small ice particles with sizes down to a few micrometers remains challenging. Here the authors report the development of a system that measures the size and shape of small ice particles using a novel combination of high-resolution imaging and high-speed automated image classification. The optical system has a pixel resolution of 0.2 μm and a resolving power of approximately 1 μm. This imaging instrument is integrated into a cryogenic flow tube that allows precise control of experimental conditions. This study also describes an automated method for the high-speed analysis of high-resolution particle images. Each particle is located in the image using a Sobel edge detector, the border is vectorized, and a polygon representing the border is found. The vertices of this polygon are expressed in complex coordinates, and an analytic implementation of Fourier shape descriptors is used for piecewise integration along the edges of the polygon. The authors demonstrate the capabilities of this system in a study of the early-stage growth of ice particles, which are grown for approximately 1 min at fixed temperature and saturated water vapor concentrations in the cryogenic flowtube. Ice particle shapes and size distributions are reported and compared with habit diagrams found in the literature. The capability of the shape recognition system is verified by comparison with manual classification.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".