A new approach to understanding fluid mixing in process-study models of stratified fluids
Bibliographic record
Abstract
Well established energy-based methods of quantifying diapycnal mixing in process-study numerical models are often used to provide information about when mixing occurs, and how much mixing has occurred. However, describing how and where this mixing has taken place remains a challenge. Moreover, methods based on sorting the density field struggle with under resolution and uncertainty as to the definition of the reference density when bathymetry is present. Here, an alternative method of understanding mixing is proposed. Paired histograms of user selected variables (which we abbreviate USP) are employed to identify mixing fluid, and are then used to display regions of fluid in physical space that are undergoing mixing. Here, two case studies are presented to showcase this method: shoaling internal solitary waves and a shear instability in cold water influenced by the nonlinearity of the equation of state. For the first case, the USP method identifies differences in the mixing processes associated with different internal solitary wave breaking types, including differences in the horizontal extent and advection of mixed fluid. For the second case, the method is used to identify how density, and passive tracers are mixed within the core of the asymmetric cold-water Kelvin-Helmholtz instability.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".