An attempt to explain rain gush formation: the ionic wind approach
Bibliographic record
Abstract
Abstract Based upon experimental observation in the laboratory, we propose that ionic wind from corona discharge inside a thundercloud would play an important role in producing a rain gush. A cyclic chain of events inside a super-saturated environment in a thundercloud is proposed, each event enhancing the successive ones until lightning occurs. These successive events are collision between snowflakes and rimers, charge separation, corona discharge, ionic wind originating from the positively and negatively charged masses of cloud, vortex motion and turbulence when mixed with the updraft, more collision, more charge separation, stronger corona discharge, and so on. Meanwhile, avalanche ionization would produce more cloud condensation nuclei resulting more precipitation and hence rimers formation in the super-saturated environment. More collision in the buoyant turbulence would lead to more fusion of droplets and the formation of larger rimers. The cyclic processes would repeat themselves until the electric field between the two oppositely charged masses of cloud was strong enough to induce a lightning breakdown. There would be no more ionic wind, hence, much less buoyant turbulence. The updraft alone would not be sufficiently strong to support larger rimers which would fall down ‘suddenly’ to the earth surface as a rain gush.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".