Wave-current interactions in a high-flow tidal channel
Bibliographic record
Abstract
High-flow tidal channels (Re~O(108)) are characterized by strong bidirectional currents (3 m/s) and high turbulence intensities. As in most coastal environments, these strong flows are affected by wind and waves, especially during severe weather events. The combined occurrence of strong currents and wind-generated surface gravity waves is a nonlinear coupled process in which both the currents and the incident wave field are modified. In this work, the evolution of mean wave parameters in a high-flow tidal channel is evaluated for various wave and tidal flow conditions. Synchronous current, turbulence, and surface altimeter data measured by a bottom-mounted acoustic Doppler current profiler (ADCP) deployed in Grand Passage, a tidal channel within the Bay of Fundy in eastern Canada, are analyzed. Results indicate that wave growth and wave propagation are coupled with the magnitude and relative direction of the current. During each measured high-wind event, the significant wave height consistently increased, and the wavelength decreased, as the current magnitude increased when locally generated waves opposed the currents. To the contrary, the significant wave height was drastically reduced when the waves follow the currents even for a small current magnitude. In addition, elevated turbulence kinetic energy and vertical plumes of elevated acoustic backscatter amplitude were observed in the upper water column during high-wind events, likely bubbles injected into the free surface by breaking waves (whitecaps) and transported through the water column by the turbulence generated by tidal currents. Obtained results allow to identify when and where wave-current interactions are significant, what their implications are for the complex dynamics of tidal channels, and suggest that currents must be incorporated into forecast wave models to improve local sea state predictions and consequently navigation safety. 
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".