Sediment transport modeling in Lake Ontario embayments: Impacts on fish spawning substrates
Bibliographic record
Abstract
• Effects of wave-induced bed shear stress vary spatially in enclosed embayments, with higher values near lake-bay connections. • Deposition is more prevalent in the inner areas of bays, where wave action is weaker. • Modeled erosion zones coincide with known Cisco ( Coregonus artedi ) spawning sites, supporting hypotheses concerning the interaction of sediment dynamics with spawning habitat. • Delft3D-SWAN simulations provide critical insights to assess sedimentation impacts on fish spawning grounds in wind-wave influenced systems. Anthropogenically-driven sedimentation changes have had adverse environmental impacts on aquatic environments, including reductions in fish spawning habitats in embayments worldwide. This study was motivated by the need to understand the impacts of waves and current-driven sedimentation patterns on traditional spawning areas and their effect on sustainable fish reproduction in the Great Lakes. Coupled hydrodynamic, wave, and sediment transport models were developed within the Delft3D-SWAN (DS) framework to predict sedimentation patterns in two embayments in Lake Ontario, Sodus Bay and Chaumont Bay, that have been historically important fish spawning habitats. These bays, with distinct geomorphic characteristics and connectivity to Lake Ontario, offer an opportunity to examine how wind-generated waves and currents impact bed shear stress and subsequent sedimentation patterns. Areas experiencing greater wave-induced bed shear stress were identified and compared between the two bays. Simulated sediment transport patterns showed notable erosion near the lake-bay connections and increased deposition in the inner areas of both embayments. Observed Cisco embryo deposition corresponded to regions of high sheer stress and lower sedimentation, indicating physical attributes in those areas that are important for embryo survival. These results show where sediment settling and erosion occur in the two bays and highlight potential impacts on traditional spawning areas.
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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) | 0.004 | 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".