Potential for Local Fertilization: A Benthocosm Test of Long-Term and Short-Term Effects of Mussel Excretion on the Plankton
Bibliographic record
Abstract
Mussel aquaculture has expanded worldwide and it is important to assess its impact on the water column and the planktonic food web to determine the sustainability of farming practices. Mussel farming may affect the planktonic food web indirectly by excreting bioavailable nutrients in the water column (a short-term effect) or by increasing nutrient effluxes from biodeposit-enriched sediments (a long-term effect). We tested both of these indirect effects in a lagoon by using plankton-enclosing benthocosms that were placed on the bottom of a shallow lagoon either inside of a mussel farm or at reference sites with no history of aquaculture. At each site, half of the benthocosms were enriched with seawater that had held mussels (excretion treatment), the other half received non-enriched seawater as a control treatment. We monitored nutrients ([PO43-] and [NH4+]), dissolved oxygen and plankton components (bacteria, the phytoplankton and the zooplankton) over 5 days. We found a significant relationship between long-term accumulation of mussel biodeposits in sediments, water-column nutrient concentrations and plankton growth. Effects of mussel excretion were not detected, too weak to be significant given the spatial and temporal variability observed in the lagoon. Effects of mussels on the water column are thus likely to be coupled to benthic processes in such semi-enclosed water bodies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".