Organic enrichment at salmon farms in the Bay of Fundy, Canada: DEPOMOD predictions versus observed sediment sulfide concentrations
Bibliographic record
Abstract
A model for predicting benthic impacts of fish farms (DEPOMOD) was used to predict organic carbon deposition rates at 6 salmon farms in the southwestern New Brunswick (SWNB) area of the Bay of Fundy, Canada. Model predictions of the seafloor area with elevated deposition rates were compared to the areas of seafloor with elevated observed sulfide concentrations. DEPOMOD predictions with resuspension appeared to overestimate the rate of resuspension of waste particles where current speeds were moderate to high; therefore, model runs without resuspension were used for comparisons. There were no consistent relationships between current speeds and the predicted (without resuspension) area with elevated deposition rates and the areas with elevated sulfide concentrations. There was a positive relationship between the areas with elevated deposition rates and the areas with elevated sulfide concentrations at 3 sites, with a better fit when the DEPOMOD runs used average daily feeding rates during 1 mo periods including the date of sediment sampling (compared to average feeding rates during 3 mo feeding periods ending near the date of sampling). Because the predicted area with elevated deposition rates (without resuspension) was strongly correlated with the feeding rate, it is important that the appropriate feeding rate be used in model runs. At sediment sampling stations where predicted deposition rates were low, sulfide concentrations were usually low; however, at sampling stations where predicted deposition rates were elevated, sulfide concentrations showed high variability. Implications for the use of DEPOMOD for management of the salmon aquaculture industry in SWNB are discussed.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".