A model of aquaculture biodeposition for multiple estuaries and field validation at blue mussel (<i>Mytilus edulis</i>) culture sites in eastern Canada
Bibliographic record
Abstract
Development of mariculture in Canadian waters has outpaced the ability of regulators to adequately assess environmental impacts and coexistence with other resource users. In eastern Canada, suspended longline culture of blue mussels (Mytilus edulis) leads to depletion of seston and subsequent biodeposition of feces and pseudofeces. Based on the need to evaluate aquaculture effects over multiple farms, a model was developed to compare the rate of mussel egestion with the scale of culture and tidal flushing of particulate waste from estuarine waters. Egestion was calculated using a bioenergetic submodel, and tidal flushing was determined with a tidal prism method. A short-term field program of particle sensing and sediment trapping was undertaken in Tracadie Bay and Savage Harbour (Prince Edward Island) to examine model assumptions and for validation. A finite element model was used to verify tidal prism calculations. Expressing model output as sedimentation rate, predicted biodeposition in Tracadie Bay was less than that estimated from field results but within the range of estuary-wide variation. In Savage Harbour, the egestion model overestimated biodeposition, likely because culture density on leased areas was sparse. A ranking of sites based on susceptibility to culture impacts was devised for multiple culture 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".