Bibliographic record
Abstract
Abstract The cloud‐matrix method for describing the mutual cloud‐coverage relationship between any two levels is systematically discussed. A general method is devised for calculating the effective cloud emissivity for maximum‐random overlap clouds. For several cloud configurations with extreme variation in fractional cloud amounts, the errors are generally very small (<5%). The radiative‐transfer process that corresponds to the random‐overlap cloud scheme is discussed. Compared with the purely random clouds scheme, the maximum‐random overlap scheme always produces a smaller cooling rate in the lower layers of a cloud block and a smaller downward flux. The difference in cooling rate can be about 3 K d−1 and the difference in the downward flux near the surface can be as large as 20 W m−2. The calculations show that the scheme of effective cloud emissivity commonly used in general‐circulation models could cause underestimation of cloud cooling rate. The clear‐sky and the cloudy‐sky radiation field can be obtained through a single calculation process but with different water‐vapour profiles. The results show that for the all‐sky case the separate treatment of the water‐vapour profile for clear and cloudy portions makes only a very small difference in the cooling rate and upward flux at the top of the atmosphere in comparison with the results of an averaged water‐vapour profile.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".