Roles of Shear and Convection in Driving Mixing in the Ocean
Bibliographic record
Abstract
Abstract Using field, numerical, and laboratory studies, we consider the roles of both shear and convection in driving mixing in the interior of the density‐stratified ocean. Shear mixing dominates when the Richardson number Ri < 0.25, convective mixing dominates when Ri > 1.0, and in the intermediate regime when 0.25 < Ri < 1.0 both shear and convection can contribute to mixing. For pure shear mixing the mixing efficiency Rif approaches 0.5, while for pure convective mixing the mixing efficiency Rif approaches 0.75. The diapycnal diffusivities for the two mechanisms are given by very different expressions. Despite these complexities, a simple mixing length model using the mean flow shear S provides robust estimates of diffusivity across the range 0 < Ri < 2. To account for the roles of both shear and convection over this range of Ri, we also formulate a modified version of the empirical KPP model for parameterizing ocean mixing in numerical models.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".