Shear Flow Instabilities and Unstable Events Over the North Bay of Bengal
Bibliographic record
Abstract
Abstract A year‐long mooring data are used to study the upper ocean unstable events and instabilities at 18°N 89°E, which is a climatologically important region in the North Bay of Bengal. Near‐surface stability is studied from the context of the buoyancy frequency normalized shear (Vz/N) and reduced shear (S2−4N2) which are convenient measures to quantify flow stability, compared to the more widely used Richardson number (Ri). The analysis is carried out across three contrasting time periods, the monsoon, postmonsoon, and the winter of year 2012. Although it is well known that the flow stability changes from stable to unstable at Ri = Ricr=0.25, the relative importance of the perturbations of shear and buoyancy frequency in driving the unstable events is not well studied over the open oceans and more particularly over the Bay of Bengal. At 18°N, 89°E both higher than average shear and lower than average buoyancy frequency perturbations are crucial in driving the unstable events during the summer and premonsoon period. However, at increasing depths, the influence of shear perturbations becomes more dominant. Invoking the Miles‐Howard criteria for flow instability, it is seen that during the postmonsoon period, the buoyancy frequency perturbations are more critical than shear perturbations in driving the unstable events. In winter, the unstable events are influenced by both the buoyancy frequency and shear perturbations.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".