Stirring by Small-Scale Vortices Caused by Patchy Mixing
Bibliographic record
Abstract
Abstract Evidence is presented that lateral dispersion on scales of 1–10 km in the stratified waters of the continental shelf may be significantly enhanced by stirring by small-scale geostrophic motions caused by patches of mixed fluid adjusting in the aftermath of diapycnal mixing events. Dye-release experiments conducted during the recent Coastal Mixing and Optics (CMO) experiment provide estimates of diapycnal and lateral dispersion. Microstructure observations made during these experiments showed patchy turbulence on vertical scales of 1–10 m and horizontal scales of a few hundred meters to a few kilometers. Momentum scaling and a simple random walk formulation were used to estimate the effective lateral dispersion caused by motions resulting from lateral adjustment following episodic mixing events. It is predicted that lateral dispersion is largest when the scale of mixed patches is on the order of the internal Rossby radius of deformation, which seems to have been the case for CMO. For parameter values relevant to CMO, lower-bound estimates of the effective lateral diffusivity by this mechanism ranged from 0.1 to 1 m2 s−1. Revised estimates after accounting for the possibility of long-lived motions were an order of magnitude larger and ranged from 1 to 10 m2 s−1. The predicted dispersion is large enough to explain the observed lateral dispersion in all four CMO dye-release experiments examined.
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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 | 0.000 | 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".