Bibliographic record
Abstract
Lake Erie is the shallowest of the Great Lakes with a unique bathymetry making it susceptible to storm surges and sediment resuspension. Surface waves effects these events which has an impact on biological diversity and surrounding communities. The simulation of surface waves is important for developing a better understanding of their effects on Lake Erie, and in this study a coupled DELFT3D-SWAN model was used to simulate waves during a major storm event. Significant wave height data from buoys were used to compare to the modelled wave heights and validate the model. The bottom friction coefficient was adjusted to determine the effects of the shallow depth of Lake Erie on the simulated wave height. During weather conditions in Lake Erie defined as winds between 2.50 m/s and 15.0 m/s, changing the bottom friction coefficient has a limited effect on modelled of significant wave height. During weather events with winds greater than 15.0 m/s, bottom friction does influence the significant wave height. The stronger winds generate large waves which results in more energy loss from bottom friction. The bottom friction coefficient is different for each basin when investigating wave heights larger than 3.00 m. For the western, central, and eastern basin, the recommended JONSWAP bottom friction is different due the different bathymetry of each basin. When simulating the lake during typical conditions or when investigating all three basins, a value of 0.067 m 2 /s 3 is recommended as it is the most accurate of the simulations with a root mean square error (RMSE) = 0.33 m and a R 2 = 0.81. The model performs with a similar error to previous Great Lakes wave models making it a valid model to use to simulate waves in Lake Erie.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".