Atmospheric Dc Corona Effect Ionization As A Potential Tool For Aerosol Deposition: An Experiment
Bibliographic record
Abstract
High concentrations of ions produced by cosmic rays have an effect on the fair weather electric field which may produce significant and observable changes in local aerosol population properties. Cosmic ray ions may lower nucleation barriers promoting charged nanoparticle growth into the Aitken range and even beyond 100 nm to become cloud condensation nuclei. A twofold assumption was made. On one hand, it was hypothesized that artificially generated direct current corona effect ions would become attached to existing aerosols and these charged aerosols would be far more effective than neutral aerosols in growing via condensation, coagulation and collision which would consequently enhance the deposition rate. On the other hand, the ions may behave as catalyzers of cloud microphysical processes if they reach the cloud bases. This paper evaluates the results obtained in an experiment designed to verify the enounced hypothesis. An ionization station was installed about 8 miles south of downtown Laredo, Texas, in order to measure the impact of unipolar, corona effect ionization on aerosol population and some meteorological phenomena. The station was operated from October, 2005 through August, 2007. Real time airborne spectrometer measurements were obtained and meteorological data were recorded. Data analyzed since the conclusion of the Laredo experiment produced no evidence to support the assumption that ionization had an impact on precipitation, but the hypothesis that ionization does produce gravitational deposition of atmospheric particles was supported by the airborne measurements performed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".