Mixing in downslope flows in the ocean ‐ plumes versus gravity currents
Bibliographic record
Abstract
ABSTRACT The nature of downslope flows into stratified environments as revealed by laboratory experiments is described, and the results are then applied to interpret particular downslope flows into the ocean. In the labora-tory, non-rotating downslope flows can be divided into two main types: detraining gravity currents over suffi-ciently gentle slopes, where the buoyancy force of the dense downflow is mainly balanced by bottom drag, and entraining plumes over sufficiently steep slopes, where the buoyancy force is balanced by vigorous entrainment of environmental fluid from above. This mixing character of the flow is determined by the bottom slope, the drag coefficient and the buoyancy number B = QN3/G2, where Q and G are the volume flux and buoyancy of the down-flow and N is the buoyancy frequency of the environment. These experiments may be applied to situations in the ocean where the flow is in approximate geostrophic balance with its transverse pressure gradient, and the para-meters are applied to the flow path on the slope. Examples are provided for a number of downslope flows in var-ious locations, including the Red Sea outflow, the Mediterranean outflow into the Black Sea and the Atlantic, the Denmark Strait overflow and the outflow from the Ross Sea. RÉSUMÉ [Traduit par la rédaction] Nous décrivons la nature des écoulements descendants dans les environnements stratifiés en nous basant sur des expériences en laboratoire et nous nous servons ensuite des résultats pour interpréter certains écoulements descendants dans l’océan. Au laboratoire, on peut regrouper les
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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) | 0.001 | 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".