Bibliographic record
Abstract
Abstract In this article, the data collected over 6 yr of daily observations at a network of aircraft sounding (31 stations) in the former Soviet Union, and the data collected by Canadian researchers in field campaigns in the 1990s, are reanalyzed and compared with each other. To describe the cloud phase structure (CPS), the notion of the cloud phase index (CPI)3 is used; that is, the local mass fraction of the ice particles in the total (water + ice) water content. It is concluded that the average distribution of the (CPI)3 values in clouds depends mainly on the temperature, the cloud types, and the scale of averaging. If these characteristics remain unchanged the geographic and seasonal variations of the phase structure are small. It is shown that for averaging scales of the order of 100 m, the frequency of occurrence of liquid clouds [(CPI)3 = 0] varies from approximately 60% at 0°C to 5% at −35°C, and that of the ice clouds from about 5% to 60%. The frequency of occurrence of the mixed clouds only weakly depends on temperature, varying within 30%–40%. The dependence of the cumulative (CPI)3 distribution on temperature in the interval 0.1 < (CPI)3 < 0.7 is close to linear. For stratiform clouds (without going into further details) the coefficients of the linear parameterization are found as a function of temperature. Knowing the (CPI)3 distribution allows one to also estimate the humidity in clouds. The most urgent challenges for the experimental studies of the cloud phase structure are formulated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".