Combined models of growth, waste production, dispersal and deposition from cage-cultured Atlantic salmon to predict benthic enrichment
Bibliographic record
Abstract
Models of particulate waste production and deposition can be used in performance-based management approaches as cost-effective tools to assess environmental effects of open-pen finfish aquaculture. XLDEPMOD is an MS Excel® spreadsheet-based depositional model for predicting particulate organic carbon (POC) waste production and sedimentation from net-pen cultured finfish. Calculations are based on temperature-dependent fish growth and mass-balance calculations of feed input, growth, respiration and 3 size classes of feces. Depth-average and near-bottom directional currents are used to determine waste dispersion by fitted Gaussian distribution functions. Near-bottom velocity and substrate-based resuspension thresholds and loss of deposited waste due to decomposition and consumption by wild fish and invertebrates are used to calculate net POC sedimentation. The model was applied to 2 Atlantic salmon farms in southwestern Bay of Fundy, Canada. Sensitivity analysis showed that reduction in waste flux due to resuspension depends on the magnitude of current and wave-driven bottom shear and mass fractions of feces with different settling velocities. Depending on depth, current speed, substrate type and fecal mass fractions, resuspension can remove up to 80% of deposited waste from under net-pens. Steep gradients with high rates (>5 g POC m-2 d-1) of sedimentation predicted under and close to cages and lower rates (<1 g POC m-2 d-1) >50 m away are consistent with published DEPOMOD results and sediment trap observations at the farm sites. The model can be used by regulators to determine if acceptable environmental standards for benthic impacts due to waste deposition from salmon aquaculture are being maintained.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".