Characterization and first results of an ice nucleating particle measurement system based on counterflow virtual impactor technique
Bibliographic record
Abstract
Abstract. A specific instrument combination was developed to achieve a better microphysical and chemical characterization of atmospheric aerosol particles that have the potential to act as ice nucleating particles (INP). For this purpose a pumped counterflow virtual impactor system called IN-PCVI was set up and characterized to separate ice particles that had been activated on INP in the Fast Ice Nucleus Chamber (FINCH) from interstitial, non-activated particles. This coupled setup consisting of FINCH (ice particle activation and counting), IN-PCVI (INP separation and preparation), and further aerosol instrumentation (INP characterization) had been developed for the application in field experiments. The separated INP were characterized on-line with regard to their total number concentration, number size distribution and chemical composition, especially with the Aircraft-based Laser Ablation Aerosol Mass Spectrometer ALABAMA. Moreover, impactor samples for electron microscopy were taken. Due to the coupling the IN-PCVI had to be operated with different flow settings than known from literature, which required a further characterization of its cut-off-behavior. Taking the changed cut-off-behavior into account, the INP number concentration measured by the IN-PCVI system was in good agreement with the one detected by the FINCH optics for water saturation ratios up to 1.01 (ice saturation ratios between 1.21–1.34 and temperatures between −18 and −26 °C). First field results of INP properties are presented which were gained during the INUIT-JFJ/CLACE 2013 campaign at the high altitude research station Jungfraujoch in the Bernese Alps, Switzerland (3580 m a.s.l.).
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".