Hydrodynamic Modelling of Storm Surge on Lake Erie
Bibliographic record
Abstract
Lake Erie is the second smallest and shallowest Great Lake, with a gradual bathymetric profile and alignment of the lake's longitudinal axis with common wind patterns.The physical characteristics of Lake Erie result in major storm surge events along the lake's shoreline, having significant social and economic consequences.To understand the sensitivity of Lake Erie to physical and atmospheric variables, a highresolution storm surge model was developed using Delft3D.This model aimed to accurately hindcast an extreme water level event which had a maximum water level differential of 4.27 m and a maximum windspeed of ~25 m/s.The Delft3D model exhibited low sensitivity to lake bottom friction and high sensitivity to wind drag as demonstrated through iterative sensitivity analysis.The final model predicted a maximum water level differential of 3.68 m with a total Root Mean Square Deviation (RMSD) of 0.14 m for the fiveday simulation period.Model results are comparable to existing hydrodynamic models for Lake Erie and fall within the upper range of maximum water level hand calculations.Further calibration of wind drag parameters is recommended to improve model performance.Once this model is developed further it may be applied to compliment and improve water level forecasts made by an existing operational hydrodynamic forecast system called COASTLINES (Canadian cOASTal and Lake forecastINg modEl System).This system will provide improved water level predictions using near-real time atmospheric forcing for lake management and public awareness.
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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 | 0.000 |
| 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 | 0.001 | 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".