Internal Wave Generation by a Combination of Tidal and Steady Currents
Bibliographic record
Abstract
In the presence of topography, two main contributors for internal wave energy are tide-topography interaction transferring energy from the barotropic tide to internal tides, and lee wave generation when geostrophic currents or eddying abyssal flows interact with topography. In the past few decades, many studies considered the respective contribution of the oscillating flows or steady background flows, but few investigations have considered both. In this talk, we consider the joint effects of tidal and steady currents to investigate internal wave generation and propagation on the Amazon shelf, a hotspot for internal solitary wave (ISW) generation. The Amazon Shelf is off the mouth of the Amazon River in the southwest tropical Atlantic Ocean, affected by strong tidal constituents over complex bottom bathymetry and a strong western boundary current, the North Brazilian Current (NBC). Both satellite observations and numerical modelling are used in this study. Satellite observations provide a clear visualization of the wave characteristics, such as temporal and spatial distributions, propagating direction and its relation to background currents. Based on parameters from satellite observations and reanalysis dataset, we set up a model to numerically investigate the dynamics of the ISW generation. We demonstrate that the small-scale topography contributes to a rich generation of along-shelf propagating ISW, which significantly contribute to the ocean mixing and potentially cause sediment resuspension. Moreover, the ISW-induced currents also contribute to the sea surface wave breaking as observed by satellite measurements. In addition, statistics based on a decade of satellite images and numerical investigations on seasonal variations of the ISWs and the NBC improve our understanding of the generation and evolution of these nonlinear internal waves in the presence of background currents.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".