Technique for Ice Crystal Particle Size Measurements and Results for the National Research Council of Canada Altitude Ice Crystal Test System
Bibliographic record
Abstract
This paper describes the equipment, analysis methods and results obtained for particle size measurements based on a particle imaging velocimetry (PIV) system in which a short duration laser pulse is used to backlight airborne particles. This produces high quality and high resolution images of fast moving airborne particles in a non-intrusive manner. This imaging technique is also used to examine particle morphology and 2D particle trajectory and velocity. The image analysis methods are outlined and validation test results discussed which show the measurement of reference glass beads between 20 and 400 microns were generally to within their stated size. As well, validation testing using known icing wind tunnel droplet distributions were compared with Spraytek 2000 Malvern droplet size measurements and showed agreement of the MVD's to be within ±5% for distributions having nominally 20, 40 and 80 micron MVD's. Implementing this non-intrusive shadowgraphy technique at the NRC ice crystal test system installed at its research altitude test facility (RATFac) showed ice particle size distributions could be generated from approximately 60 to700 micron median mass diameters (MMD's) with the ability to create larger particles well above 1 mm in diameter.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".