Spectral and Structure Function Estimates of Turbulence Dissipation Rates in a High-Flow Tidal Channel Using Broadband ADCPs
Bibliographic record
Abstract
Abstract Spectral and structure function methods are implemented to compute the dissipation rate ε from broadband, diverging-beam acoustic Doppler current profiler (ADCP) data collected at four sites in a high-flow tidal channel. This paper shows that middepth estimates of ε obtained from spectral and second-order structure function (SF2) methods are both lognormally distributed with comparable means and variances. Speed bin–averaged ε values agree to within 16%, depending on the site and tidal phase (ebb/flood). The close agreement between the two independent methods provides further support for the argument put forward by McMillan et al.: that is, that the factor-of-2 difference between shear probe and (spectral) ADCP estimates of ε was likely caused by spatial differences in turbulence levels. The agreement between the spectral and both second- and third-order structure function methods also supports the use of for the SF2 universal constant. Notably, however, the SF3 method was less robust for these data. Two additional aspects of the SF2 approach are examined in some detail: 1) the differences from upstream- and downstream-facing beams are shown to arise from the Reynolds stress and 2) the inability of the ADCP to resolve small-scale motions does not affect the estimates of ε but yields apparent Doppler noise levels that—counterintuitively—decrease with increasing flow speed and increasing dissipation rate. A modified SF2 method that accounts for the variance associated with the unresolved scales removes the flow speed dependence and yields noise level estimates that agree with the spectral values.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".