Modeling the effect of cage drag on particle residence time within fish farms in the Bay of Fundy
Bibliographic record
Abstract
Aquaculture farm cages have the ability to interact with local circulation due to the drag caused by the cages. We examined how the drag influences the residence time of particles within fish farms in the southwest Isles region of New Brunswick, Canada, in the Bay of Fundy using a high-resolution hydrodynamic model. To accomplish this, we parameterized the cage drag in the model and modeled flow structures at multiple spatial scales, ranging from several meters within the cages, to tens of kilometers in the adjacent open ocean. We used models with and without cage drag to demonstrate how residence time was influenced by the imposition of the cage infrastructure. Our examination indicated that the drag produced by cages is able to significantly change the residence time of particles. The magnitude of the change is strongly sensitive to the timing of tides, tidal speeds and specific locations of farms. Our results suggest that the flushing properties of the wastes from aquaculture activities—for example, feed and subsequently fecal material—are strongly related to flow properties and their interactions with cages. These results emphasize that the design of fish farms should explicitly account for the influence of physical infrastructure (i.e. cages) on depositional processes in order to try and minimize environmental effects.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".