Modelling coastal processes at Shippagan Gully inlet, New Brunswick, Canada
Bibliographic record
Abstract
This paper describes the development, calibration and application of a numerical model of the hydrodynamic and sedimentary processes at a dynamic tidal inlet known as Shippagan Gully, located on the Gulf of St-Lawrence near Le Goulet, New Brunswick. The new model has been developed to provide guidance concerning the response of the inlet mouth to various potential interventions aimed at increasing navigation safety. The new model is based on coupling the most recent CMS-Flow and CMSWave models developed by the US Army Corps of Engineers. The coupled model is capable of simulating the depth-averaged currents generated within Shippagan Gully and along the neighbouring coastline due to the effects of tides, winds and waves; the transport of non-cohesive sediments; and the resulting changes in seabed morphology. The development of the model and the steps taken to calibrate and validate it against field measurements are described. The application of the model to predict the coastal processes and the response of the inlet mouth to several storms is described and discussed. The influences of storm direction and storm surge on coastal processes is presented and discussed. The research described herein will contribute to an improved understanding of the hydrodynamics and sedimentary processes at strongly ebb-dominated tidal inlets in general and Shippagan Gully in particular.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".