Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A moist turbulence model based on the shallow Boussinesq equations with a simple condensation scheme is introduced. Its key advantage is its ability to express the major dynamical effects of moisture on turbulence while maintaining computational efficiency. Because of its simple condensation scheme and periodic boundary conditions, the majority of the computational and memory expense can go towards increased resolution. Sensitivity experiments were performed on a test case of moist bubble simulations. We find that timestep choices that satisfy the CFL condition give adequate temporal resolution. Also addressed are other numerical issues regarding spurious oscillations in fields, in particular in the moisture variables. A ‘hole-filled’ experiment, which removes the negative liquid water and partially smooths the vapour and liquid water fields, indicates that these issues are not important for such a high-resolution model and field-smoothing schemes are not worth the increase in computation expense or lowered accuracy. The moist bubble test cases also span a large range of resolutions, from 903 to 3843. The higher resolutions show shallow liquid water spectra, implying that resolution is key to correct modelling of moist atmospheric dynamics. Acknowledgements The authors would like to thank M. Waite, K. Ngan, L. Bourouiba and D. Straub for helpful discussions. Comments from an anonymous reviewer were also greatly appreciated. Support from the Natural Sciences and Engineering Research Council of Canada, through a Post-Graduate Scholarship (K.S.), and from the Canadian Foundation for Climate and Atmospheric Sciences, through the Quantitative Precipitation Forecasting network (P.B. and M.K.Y.) is gratefully acknowledged.
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.
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 | 0.000 | 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 it