Turbulence and Mixing in the Arctic Ocean’s Amundsen Gulf
Bibliographic record
Abstract
Abstract This study uses CTD and microstructure measurements of shear and temperature from 348 glider profiles to characterize turbulence and turbulent mixing in the southeastern Beaufort Sea, where turbulence observations are presently scarce. We find that turbulence is typically weak: the turbulent kinetic energy dissipation rate ε has a median value (with 95% confidence intervals in parentheses) of 2.3 [2.2, 2.4] × 10 −11 W kg −1 and is less than 1.0 × 10 −10 W kg −1 in 68% of observations. Variability in ε spans five orders of magnitude, with indications that turbulence is bottom enhanced and modulated in time by the semidiurnal tide. Stratification is strong and frequently damps turbulence, inhibiting diapycnal mixing. Buoyancy Reynolds number estimates suggest that turbulent diapycnal mixing is unlikely in 93% of observations; however, a small number of strongly turbulent mixing events are disproportionately important in determining net buoyancy fluxes. The arithmetic mean diapycnal diffusivity of density is 4.5 [2.3, 14] × 10 −6 m 2 s −1 , three orders of magnitude larger than that expected from molecular diffusion. Vertical heat fluxes are modest at O (0.1) W m −2 , of the same order of magnitude as those in the Canada Basin double-diffusive staircase, however, staircases are generally not observed. Despite significant heat present in the Pacific Water layer in the form of a warm-core mesoscale eddy and smaller, O (1) km, temperature anomalies, turbulent mixing was found to be too low to release this heat to shallower depths.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".