Biodiversity–Ecosystem Functioning (BEF) approach to further understanding aquaculture–environment interactions with application to bivalve culture and benthic ecosystems
Bibliographic record
Abstract
Abstract Coastal benthic ecosystems may be impacted by numerous human activities, including aquaculture, which continues to expand rapidly. Indeed, today aquaculture worldwide provides more biomass for human consumption than do wild fisheries. This rapid development raises questions about the interactions the practice has with the surrounding environment. In order to design strategies of sustainable ecosystem exploitation and marine spatial planning, a better understanding of coastal ecosystem functioning is needed so that tools to quantify impacts of human activities, including aquaculture, may be developed. To achieve this goal, some possible directions proposed are integrated studies leading to new concepts, model development based on these concepts and comparisons of various ecosystems on a global scale. This review draws on existing literature to (i) briefly summarize the major ecological interactions between off‐bottom shellfish aquaculture and the environment, (ii) introduce research on the influence of benthic diversity on ecosystem functioning (BEF relationships) and (iii) propose a holistic approach to conduct aquaculture–environment studies using a BEF approach, highlighting the need for integrated studies that could offer insights and perspectives to guide future research efforts and improve the environmental management of aquaculture.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".