Modeling of waste outputs in the aquatic environment from a commercial cage farm under neotropical climate conditions
Bibliographic record
Abstract
The present study used a bioenergetics modeling approach to estimate the solid and dissolved waste outputs of a Nile tilapia Oreochromis niloticus net-cage farm. Historical production data for 30 cages were obtained from a commercial farm in the Chavantes Reservoir, São Paulo State, Brazil. In addition, an experiment was carried out in 4 net-cages at the farm to validate this dataset and collect fish samples. A total of 32400 tilapias with an initial weight averaging 35 ± 2.73 g were equally distributed in the experimental net cages. After 210 d, the fish showed a final individual weight of ~789 ± 5.12 g. Fish growth performance was monitored, and body composition was analyzed each month. Digestibility trials of commercial diets used for juvenile stages JVI and JVII and market weight were performed. Relationships of body weight with body content data of water, protein, fat, ash, gross energy, phosphorus, and nitrogen were evaluated by regression analysis. The total digestible energy requirement and estimated residues of the fish were assessed using the factorial bioenergetics model, adapted to the growing conditions of a neotropical reservoir. The model estimated ~320 kg of total solid waste released per tonne of tilapia, including ~10 kg of solid nitrogen and ~5 kg of solid phosphorus. Approximately 3 and 47 kg of dissolved phosphorus and nitrogen, respectively, were estimated per tonne of tilapia. The bioenergetics model is a valuable and equitable method for assessing and monitoring waste outputs. It can improve the nutritional and environmental efficiency of aquaculture activities, helping producers to reduce feed costs while strengthening the environmental sustainability 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".