Performance measures and models for open‐water integrated multi‐trophic aquaculture
Bibliographic record
Abstract
Abstract Qualifying and quantifying nutrient flows within open‐water Integrated Multi‐Trophic Aquaculture ( IMTA ) systems is necessary to determine transfer efficiencies and to assess overall system performance. There are numerous empirical performance metrics, such as spatially defined growth and nutrient sequestration, which may have application. When used in combination with modelling techniques, empirical approaches can be a powerful tool for system assessment or prediction. Simple empirical growth models, such as the thermal‐growth coefficient ( TGC ) and scope for growth ( SFG ), are applicable to aquatic animals and can include nutritional mass‐balance approaches to estimate nutrient loads. Comparable empirical growth models exist for seaweeds. Mechanistic‐based dynamic growth and reproduction models, such as Dynamic Energy Budget ( DEB ), are more complex, but have application beyond site‐specific empirical models and can, therefore, be included into larger ecosystem models for application to IMTA . Proximity, ecological transfer efficiencies, particle dynamics, species culture ratios and the timing of multi‐species production cycles can have profound implications for IMTA effectiveness and require careful consideration for system assessment. This review provides a pragmatic evaluation of performance measures and models to assess nutrient transfer and growth in open‐water IMTA systems.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".