Ionization, Charging and Electric Field Effects on Cloud Particles in the CLOUD Experiment
Bibliographic record
Abstract
Ice crystals and frozen droplets play an important role in atmospheric charging and electrification processes, particularly by collision and aggregation. The dynamics of charged particles in the atmosphere can be modulated by Galactic Cosmic Rays (GCR). High electric fields also affect the alignment of charged particles, allowing more time for interactions. The CLOUD (Cosmics Leaving OUtdoor Droplets) experiment at CERN has the ability to conduct ionization, charging and high electric field experiments on liquid or ice clouds created in the chamber by adiabatic pressure reductions. A pion secondary beam from the CERN Proton Synchrotron is used to ionize the molecules in the chamber, and Ar+ Corona Ion Generator for Atmospheric Research (CIGAR) is used to inject unipolar charged ions directly into the chamber. A pressurized airgun provides rapid pressure shocks inside the chamber and induces charged ice nucleation. The cloud chamber is accompanied by a variety of analysing instruments e.g. a 3View Cloud Particle Imager (3V-CPI) coupled with an induction ring, a Scattering Intensity Measurements for the Optical detection of icE (SIMONE) and a Nano-aerosol and Air Ion Spectrometer (NAIS). Using adiabatic expansion and high electric fields we can replicate the ideal conditions for adhesion, sintering and interlocking between ice crystals. Charged cloud particles produced measurable variations in the total induced current pulse on the induction ring. The most influential factors comprised initial temperature, lapse rate and charging mechanism. The ions produced in the chamber may deposit onto larger particles and form dipoles during ice nucleation and growth. The small ion concentration was monitored by the NAIS during these runs. Possible short-term aggregates or alignment of particles were observed in-situ with the SIMONE. These and future chamber measurements of charging and aggregation could shed more light on the ambient conditions and dynamics for electrification in the atmosphere.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 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 teacher head, 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".