Inertial Oscillations and Frontal Processes in an Alboran Sea Jet: Effects on Divergence and Vertical Transport
Bibliographic record
Abstract
Abstract Vertical transport pathways in the ocean are still only partially understood despite their importance for biogeochemical, pollutant, and climate applications. Detailed measurements of a submesoscale frontal jet in the Alboran Sea (Mediterranean Sea) during a period of highly variable winds were made using cross‐frontal velocity, density sections and dense arrays of surface drifters deployed across the front. The measurements show divergences as large as ±f implying vertical velocities of order 100 m/day for a ≈ 20 m thick surface layer. Over the 20 hr of measurement, the divergences made nearly one complete oscillation, suggesting an important role for near‐inertial oscillations. A wind‐forced slab model modified by the observed background frontal structure and with initial conditions matched to the data produces divergence oscillations and pattern compatible with that observed. Significant differences, though, are found in terms of mean divergence, with the data showing a prevalence of negative, convergent values. Despite the limitations in data sampling and model uncertainties, this suggests the contribution of other dynamical processes. Turbulent boundary layer processes are discussed, as a contributor to enhance the observed convergent phase. Water mass properties suggest that symmetric instabilities might also be present but do not play a crucial role, while downward stirring along displaced isopycnals is observed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".