Theoretical investigation of mixing in warm clouds – Part 2: Homogeneousmixing
Bibliographic record
Abstract
Abstract. Evolution of monodisperse and polydisperse droplet size distributions (DSD) during homogeneous mixing is analyzed. Time-dependent universal analytical expressions for supersaturation and liquid water content are derived. For an initial monodisperse DSD, these quantities are shown to depend on a sole non-dimensional parameter. The evolution of moments and moment-related functions in the course of homogeneous evaporation of polydisperse DSD is analyzed using a parcel model.It is shown that the classic conceptual scheme, according to which homogeneous mixing leads to a decrease in droplet mass at constant droplet concentration, is valid only in cases of monodisperse or initially very narrow polydisperse DSD. In cases of wide polydisperse DSD, mixing and successive evaporation lead to a decrease of both mass and concentration, so the characteristic droplet sizes remain nearly constant. As this feature is typically associated with inhomogeneous mixing, we conclude that in cases of an initially wide DSD at cloud top, homogeneous mixing is nearly indistinguishable from inhomogeneous mixing.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".