Characterization of turbulence and validation of fine-scale parametrization in the Mediterranean Sea during BOUM experiment
Bibliographic record
Abstract
Abstract. One main purpose of BOUM experiment was to give evidence of the possible impact of submesoscale dynamics on biogeochemical cycles. To this aim physical as well as biogeochemical data were collected along a zonal transect through the western and eastern basins. Along this transect 3 day fixed point stations were performed within anticyclonic eddies during which microstructure measurements were collected over the first 100 m. We focus here on the characterization of turbulent mixing induced by internal wave breaking. The analysis of microstructure measurements revealed a high level of turbulence in the seasonal pycnocline and a moderate level below with energy dissipation mean values of the order of 10−6 W kg−1 and 10−8 W kg−1, respectively. Fine-scale parameterizations developed to mimic energy dissipation produced by internal wavebreaking were then tested against these direct measurements. Once validated a parameterization has been applied to infer energy dissipation and mixing over the whole data set, thus providing an overview over a latitudinal section of the Mediterranean sea. The results evidence a significant increase of dissipation at the top and base of eddies associated with strong near inertial waves. Vertical turbulent diffusivity is increased both in these regions and in the weakly stratified eddy core.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".