Predictive ecological modelling for suspended shellfish aquaculture systems: assessing biodeposition and benthic effects with Shellfish-DEPOMOD
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author.Numerical modelling provides an effective means to evaluate the interactions between aquaculture activities and the ecosystem. To date, modelling effort with regard to shellfish cultivation has focused primarily on predicting bivalve growth and production carrying capacity rather than environmental interactions. Modelling the near-field effects of shellfish aquaculture through biodeposition has received little attention, and consequently there is a need for effective models to predict the organic flux from culture sites to the bottom. Here, we present the application of a particle waste dispersal model to predict the near-field effects of biodeposition from suspended shellfish culture. Results will be presented on the development and application of Shellfish-DEPOMOD. The model was tested at three coastal mussel Mytilus edulis farms with differing hydrodynamic regimes in Quebec, Canada. For each site, the model results were validated by comparing predictions with observed deposition measured in situ with sediment traps. The relationship between long-term biodeposition and benthic descriptors was assessed. Overall, the model predictions compared favourably with observed sedimentation rates both in terms of flux and extent of dispersion. Alterations to the benthic community were observed at high deposition rates. Model parameter uncertainties and limitations will be discussed in the context of ecosystem-based management of marine areas
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".