Bibliographic record
Abstract
The objective of this dissertation is to develop a framework of coupling model system and apply the system (i.e., single or multiple model components) in the Great Lakes to better understand the impacts of multi-scale hydrodynamics of coastal systems. This work has included three main topics that reveal the lake dynamics from basin to nearshore scales. In the basin scale, a study of the long-term wave climate of Lake Michigan from 1979 to 2020 is conducted following the climate change over the past decades, and a high-resolution wave model is performed for hindcasting. The details of this study are included in chapter 3. Two nearshore related cases are studied in Lake Michigan and Lake Ontario, respectively. The occurrence of meteotsunamis is not uncommon in Lake Michigan, and it has significant contributions in a nearshore flooding event (i.e., meteotsunami-induced flooding). The modeling study of nearshore meteotsunami-induced flooding is produced in the Ludington, Michigan nearshore of Lake Michigan. The study applied an extremely high-resolution hydrodynamic model with localized configurations to adapt to specific features (i.e., breakwaters and wet/dry processes). Numerical experiments were examined to evaluate the recent flooding situation and additional flooding analysis was performed to represent near future climate scenarios. The study is expected to set up a framework in the future for similar work in different locations. The details can be found in chapter 4. Another phenomena related to nearshore processes is conducted on the northern shore of Lake Ontario near the town of Ajax. To resolve the transport issues (i.e., discharge plume and pollutant of concern footprint dimensions), a high-resolution, linked hydrodynamic-tracer model is applied. The model results are validated with observations and then used for qualifying the footprint dimensions. The results and suggestions for potential management aspects are given in chapter 5.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".