A three-dimensional hydrodynamic model for aquaculture: a case study in the Bay of Fundy
Bibliographic record
Abstract
Impacts of aquaculture on the local current field and the erosion of the bottom sediment in the Bay of Fundy, Canada, have been investigated with a 3-dimensional hydrodynamic model. The model is evaluated against independent observations of the current. Model results show that the presence of fish cages restricts water flow and reduces the velocity in the surface layer occupied by the cages, but enhances the water velocity in the bottom layer beneath the cages. Sensitivity studies show that the change in the flow velocity beneath the cages is sensitive to variations in the drag coefficient and the height of the fish cages. As the drag coefficient increases, the bottom velocity also increases until a steady state value is reached. For the cage height, however, the tidal speed beneath the cages first increases with cage height and then significantly decreases with further increasing height. The maximum increase in velocity occurs when the cage height is about half the local water depth (H/H0 = 0.5, where H is the cage height and H0 is the water depth). The increase in bottom velocity significantly speeds up the erosion of the bottom sediment. The model results also indicate that there is an optimal drag coefficient and an optimal cage height for a specific farm site. By utilizing the optimal drag coefficient and height, it is possible to speed up sediment erosion beneath the cages and, thus, decrease the environmental problems caused by accumulated fish farm waste.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| 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".