Effects of Pressure on Collision, Coalescence, and Breakup of Raindrops. Part II: Parameterization and Spectra Evolution at 50 and 100 kPa
Bibliographic record
Abstract
Abstract Fragment size distributions, experimentally obtained for six drop pairs colliding at 50 kPa, are parameterized similarly to the 100-kPa drop pair experiments by Low and List. This information is then introduced into a box model to allow assessment of the spectra evolution and a comparison of the two datasets taken at the two pressures. The differences in breakup patterns include the following: The contributions to mass transfer by breakup and coalescence are very similar at the two pressures, with larger values at lower pressure; the overall mass evolution is not particularly sensitive to pressure; and disk breakup plays an “erratic” role. The situation for the number concentration, however, is totally different and develops gradually. At 50 kPa there is also no three-peak equilibrium developing as for 100 kPa. The times to reach equilibrium are ∼12 h. Note that the box model does not include accretion of cloud droplets—which may well be more important than growth by accretion of fragments. Application of the new parameterization is not beneficial for low rain rates, but it is strongly recommended for large rain rates (>50 mm h−1).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".