Measuring the Dissipation Rate of Turbulent Kinetic Energy in Strongly Stratified, Low‐Energy Environments: A Case Study From the Arctic Ocean
Bibliographic record
Abstract
Abstract We compare estimates of the turbulent dissipation rate, ε , obtained independently from coincident measurements of shear and temperature microstructure in the southeastern Beaufort Sea, a strongly stratified, low‐energy environment. The measurements were collected over 10 days in 2015 by an ocean glider equipped with microstructure instrumentation; they yield 28,575 shear‐derived and 21,577 temperature‐derived ε estimates. We find agreement within a factor of 2 from the two types of estimates when ε exceeds 3 × 10 −11 W/kg, a threshold we identify as the noise floor of the shear‐derived estimates. However, the temperature‐derived estimates suggest that the dissipation rate is lower than this threshold in 58% of our observations. Further, the noise floor of the shear measurements artificially skews the statistical distribution of ε below 10 −10 W/kg, that is, in 70% of our observations. The shear measurements overestimate portions of the geometric mean vertical profile of ε by more than an order of magnitude and underestimate the overall variability of ε by at least 2 orders of magnitude. We further discuss uncertainties that arise in both temperature‐ and shear‐derived ε estimates in strongly stratified, weakly turbulent conditions, and we demonstrate how turbulence spectra are systematically modified by stratification under these conditions. Using evidence from the temperature‐gradient spectral shapes and from the observed ε distributions, we suggest that the temperature‐derived dissipation rates are reliable to values as small as 2 × 10 −12 W/kg, making them preferable for characterizing the turbulent dissipation rates in the weakly turbulent environment of this study.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".