Variations of Bottom Boundary Layer Turbulence under the Influences of Tidal Currents, Waves, and Raft Aquaculture Structure in a Shallow Bay
Bibliographic record
Abstract
High-frequency measurements of tides, waves, and turbulence were made using the bottom-mounted tripod equipped with the Nortek 6-MHz acoustic Doppler velocimetry during 20–23 February 2016 (winter) and 12–26 June 2017 (summer) in Heini Bay, Yellow Sea. The synchro-squeezed wavelet transform was applied for wave-turbulence decomposition, and an iterative procedure was developed to identify the turbulence inertial subrange in the bottom boundary layer. The analysis results reveal the dependency of the inertial subrange on the tidal current and turbulence intensities. The flood-ebb tidal flows are different between the summer and winter seasons, without and with the presence of dense raft aquaculture for kelp, respectively. In summer, the turbulent kinetic energy (TKE), turbulent Reynolds stress (TRS), and dissipation rate (ε) of TKE increase smoothly with the increasing tidal flow magnitude, and ε is approximately in balance with TKE production related to the vertical shear. The presence of heavy kelp aquaculture in winter causes the reduction in flow speeds and TRS, while keeping TKE and ε at high levels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".