Quantifying Diapycnal Mixing in an Energetic Ocean
Bibliographic record
Abstract
Abstract Turbulent diapycnal mixing controls global circulation and the distribution of tracers in the ocean. For turbulence in stratified shear flows, we introduce a new turbulent length scale dependent on χ. We show the flux Richardson number Rif is determined by the dimensionless ratio of three length scales: the Ozmidov scale LO, the Corrsin shear scale LS, and . This new model predicts that Rif varies from 0 to 0.5, which we test primarily against energetic field observations collected in 100 m of water on the Australian North West Shelf (NWS), in addition to laboratory observations. The field observations consisted of turbulence microstructure vertical profiles taken near moored temperature and velocity turbulence time series. Irrespective of the value of the gradient Richardson number Ri, both instruments yielded a median , while the observed Rif ranged from 0.01 to 0.50, in agreement with the predicted range of Rif. Using a Prandtl mixing length model, we show that diapycnal mixing can be predicted from and the background vertical shear S. Using field and laboratory observations, we show that where LE is the Ellison length scale. The diapycnal diffusivity can thus be calculated from . This prediction agrees very well with the diapycnal mixing estimates obtained from our moored turbulence instruments for observed diffusivities as large as m2s−1. Moorings with relatively low sampling rates can thus provide long time series estimates of diapycnal mixing rates, significantly increasing the number of diapycnal mixing estimates in the ocean.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".