Measuring and modelling the dispersal of salmon farm organic waste over sandy sediments
Bibliographic record
Abstract
Fish farm waste dispersal models are widely used but have only been directly validated to a limited extent. Two shallow (<20 m) Atlantic salmon farms (Bay of Meil and Quanterness) in Orkney, Scotland were studied. Bay of Meil has peak near-bed currents of 9.7 cm s-1 whereas Quanterness has flows up to 31.6 cm s-1. Sediment tray traps which allow resuspension to occur were deployed at each site. The patterns of particulate organic carbon (POC) deposition into the traps were in broad agreement with the observed water current directions and results from infaunal benthic monitoring. Despite the markedly different flow regimes at the 2 sites, most of the deposition occurred within 210 m of the cage perimeters. POC footprints were then modelled using the particle tracking model NewDEPOMOD. For Bay of Meil, a footprint was obtained using the recommended parameter defaults, but the spatial extent was too constrained compared to the sediment tray results. For Quanterness, all simulated particles were lost from the model domain and the critical erosion shear stress had to be increased to unrealistic levels to obtain a footprint. The failure to find a common set of parameter values applicable to both sites, despite their similar depths and sandy seabed, suggests that there remain unresolved issues, likely in how NewDEPOMOD handles waste resuspension. The sediment trays provided a direct method for quantifying the organic carbon deposition, facilitating direct validation of the dispersal model and demonstrating that further research is needed on fish farm waste dispersal at coarser sediment sites.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".