Three-Dimensional Numerical Simulation of Nonlinear Internal Waves in the St. Lawrence Estuary, Canada
Bibliographic record
Abstract
Nguyen, V. T., 2016. Three-Dimensional Numerical Simulation of Nonlinear Internal Waves in the St. Lawrence Estuary, Canada. In: Vila-Concejo, A.; Bruce, E.; Kennedy, D.M., and McCarroll, R.J. (eds.), Proceedings of the 14th International Coastal Symposium (Sydney, Australia). Journal of Coastal Research, Special Issue, No. 75, pp. 902–906. Coconut Creek (Florida), ISSN 0749-0208.The existence of nonlinear internal waves (NIWs) running up a sloping boundary of the Ile-aux-Lievres Island in the St. Lawrence Estuary, Canada has been revealed by the researchers at Memorial and Dalhousie Universities of Canada. Unfortunately, there is no direct field observation of the generation of the NIWs in this region in order to determine where these waves be generated and emanated from. In this paper, a nonhydrostatic three-dimensional model, MITgcm model, is modified and applied to investigate the generation and propagation of NIWs by tidal forcing over an idealized depression simplified from the topography of the North Channel in the St. Lawrence Estuary. A comparison between the numerical results and the observations shows very good qualitative agreements. The results of the simulation is not only to explain above phenomenon, but also to show the mechanism of the NIWs in this region.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".