Bibliographic record
Abstract
Abstract Ice clouds play an important role in the hydrological cycle and climate change, and their impact on water and heat cycle is crucial depending on their macro‐ and microphysical properties. As ice fog, it also affects weather conditions such as visibility. Ice clouds and ice fog usually form at cold temperatures ( T < 0 °C), and their nucleation processes usually take place at T < −5 °C. They occur extensively at colder T . They are usually composed of only ice crystals although mixed‐phase particle can be found down to −40 °C, and below that T , droplets can freeze up spontaneously. The development of ice clouds needs some level of dynamical activity, e.g. updrafts and thermal instabilities in a lifting moist air layer. Ice fog is composed of many small particles and has less water content compared to ice clouds. Its impact on economy and on Earth's heat budget is still not understood properly. In situ observation of ice cloud and their monitoring using remote‐sensing platforms such as radar, LiDAR, and satellites can improve their predictions and help develop algorithms to retrieve their physical properties. In this article, the physical properties of ice cloud and ice fog are summarized using in situ and remote‐sensing observations, also focusing on the prediction issues related to forecast and climate models.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.065 | 0.020 |
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; both teacher heads agree on what is shown here.
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".