Open‐water integrated multi‐trophic aquaculture: environmental biomitigation and economic diversification of fed aquaculture by extractive aquaculture
Bibliographic record
Abstract
Abstract Integrated multi‐trophic aquaculture (IMTA) seeks to biodiversify fed aquaculture (e.g. finfish or shrimps) with extractive aquaculture, recapturing the inorganic (e.g. seaweeds) and organic (e.g. suspension‐ and deposit‐feeders) nutrients from fed aquaculture for their growth. The combination fed/extractive aquaculture aims to engineer food production systems providing both biomitigative services to the ecosystem and improved economic farm output through the co‐cultivation of complementary species. Major rethinking is needed regarding the definition of an ‘aquaculture farm’ and how it works within an ecosystem. The economic values of the environmental/societal services of extractive species should be recognized and accounted for in the evaluation of the full value of these IMTA components. Seaweeds and invertebrates produced in IMTA systems should be considered as candidates for nutrient/carbon trading credits. While organic loading from aquaculture has been associated with localized benthic impacts, there have also been occurrences of increased biodiversity and abundance of wild species in response to moderate nutrient enrichment and the use of infrastructures as substrates. To develop efficient food production systems, it will be important to understand and use the duality of nutrients (essential when limiting/polluting when in excess) to engineer systems producing them in moderation so that they can be partially recaptured while maintaining their concentrations optimal for healthy and productive ecosystems. Measures of species diversity, colonization rates, abundance, growth and ecosystem functions with respect to nutrient partitioning and recycling, species interactions and control of diseases could represent valid indicators for the development of robust performance metrics.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".