Best practices for estimating turbulent dissipation from oceanic single-point velocity timeseries observations
Bibliographic record
Abstract
Abstract. We provide best practices for estimating the dissipation rate of turbulent kinetic energy, ε, from velocity measurements in an oceanographic context. These recommendations were developed as part of the Scientific Committee on Oceanographic Research (SCOR) Working Group #160 "Analyzing ocean turbulence observations to quantify mixing". The recommendations here focus on velocity measurements that enable fitting the inertial subrange of wavenumber velocity spectra. The method examines the measurable range for this method of dissipation rates in the ocean, seas, and other natural waters. The recommendations are intended to be platform-independent since the velocities may be measured using bottom-mounted platforms, platforms mounted beneath the ice, or platforms directly on mooring lines once the data is motion-decontaminated. The procedure for preparing the data for spectral estimation is discussed in detail, along with the quality control metrics that should accompany each estimate of ε during data archiving. The methods are applied to four 'benchmark' datasets covering different flow regimes and two instrument types (acoustic-Doppler and time of travel). Problems associated with velocity data quality, such as phase-wrapping, spikes, measurement noise, and frame interference, are illustrated with examples drawn from the benchmarks. Difficulties in resolving and identifying the inertial subrange are also discussed, and recommendations on how these issues should be identified and flagged during data archiving are provided.
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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 | 0.028 | 0.125 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.011 |
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".