Integrated multi-trophic aquaculture systems: energy transfers and food web organization in coastal earthen ponds
Bibliographic record
Abstract
Three Ecopath models were built to reproduce 3 experimental treatments carried out in earthen ponds located in Olhão, southern Portugal, to understand the energy transferred and the ecosystem state in integrated multi-trophic aquaculture (IMTA). These earthen ponds behave as simplified ecosystems or mesocosms, with well-defined borders, where the relationships between trophic groups can be described through ecosystem modeling. Different combinations of species were produced in these ponds, corresponding to the 3 treatments: (1) fish, oysters and macroalgae (FOM); (2) fish and oysters (FO); and (3) fish and macroalgae (FM). The managed species were meagre Argyrosomus regius, white seabream Diplodus sargus, flathead grey mullet Mugil cephalus, Japanese oyster Crassostrea gigas and sea lettuce Ulva spp. The results showed that the total amount of energy throughput was 15 to 17 times higher when compared with an equivalent naturalized system. The high biomass and low recycling indicated an immature system with low resilience and low stability that demands high rates of water renewal and aeration to maintain good water-quality levels for finfish production. The addition of oysters and macroalgae in the FOM treatment appeared to improve the water quality, since oysters controlled the excess of phytoplankton produced in the ponds by ingesting a fair amount of the phytoplankton, while the macroalgae helped in the absorption of excess nutrients and created a habitat for periphyton and associated macroinvertebrates. Some ecosystem attributes of the FOM ponds approached the values of the naturalized model, suggesting a possible path towards more sustainable 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".