The Stratified Inclined Duct: a new canonical laboratory experiment to study ocean turbulence and mixing
Bibliographic record
Abstract
We present a relatively recent laboratory experiment, the Stratified Inclined Duct (SID), that sustains a buoyancy-driven exchange flow and allows to accurately control and measure stratified sheared turbulence. The submesoscale turbulent mixing of momentum, heat, and salinity in the ocean have a leading-order but poorly constrained large-scale impact. We argue that SID may serve as a fruitful testbed for studying these processes, especially near boundaries, such as in estuaries.First, we introduce SID, which consists of two large (400 litres) reservoirs containing salt solutions connected by a long rectangular duct. The long-lasting exchange within the duct has a Reynolds number of order Re ~ 1,000-10,000. The apparatus can be tilted at a small angle θ with respect to the horizontal, which energises the flow and increases turbulence levels, due to the emergence of 'hydraulic control'.Second, we present high-resolution experimental measurements of the three-dimensional velocity and density field within the duct which allow to delve into the energetics of stratified turbulence. SID uniquely allows the experimenter to control the level of turbulent kinetic energy dissipation, and to sweep through increasingly turbulent regimes by varying the key product Re*θ. The levels of turbulent intensity are comparable to those found in moderately turbulent patches in the ocean.Third, we demonstrate that a data-driven analysis combining automated image analysis, data reduction and unsupervised clustering discovered previously unsuspected patterns in a large SID turbulence dataset. Multiple types of energetic turbulence were found, as well as intermittent turbulence that cycles between these types through distinct transition pathways. We argue that this data-driven identification of turbulence is a stepping stone towards better physics-based parameterizations.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".