Bibliographic record
Abstract
Processes related to water in the atmosphere lead to severe uncertainties in weather forecasting and climate research. Atmospheric water vapor and cloud water strongly influence the Earth’s energy budget through, e.g., energy conversions associated with phase changes, fluid dynamical effects associated with buoyancy, and through their influence on radiative transfer properties of the atmosphere. Given the critical green-house effect of water vapor, it seems astounding that climate modellers cannot with certainty state whether the Earth’s cloud system has a positive or negative influence on the global mean temperature. The formation of clouds involves small-scale processes currently unresolved by climate models, and thus cloud cover is one of the main sources of uncertainty. This large uncertainty has its roots in the extremely wide range of length and time scales associated with moist processes, which pose an equally wide range of challenges to mathematical and computational modelling. New and innovative methods, modeling frameworks, efficient computational techniques, and complex statistical data analysis procedures as well as their mathematical analysis are urgently needed in order to make progress in this new field – from the mathematicians point of view. One of the main goals of this workshop is to show the path forward for current and future applied mathematical scientists, to work hand in hand across the disciplines of mathematics, physics, and atmospheric science, in order to tackle the complex problem of dynamical and thermodynamical processes associated with clouds and moisture, both from the theoretical and the applied view points.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".