Evaluation of the Efficiency of Duckweeds, Lemna sp. and Spirodela sp., in the Treatment of Tilapia Effluents
Bibliographic record
Abstract
Farming aquatic plants can be used as an alternative in the treatment of effluents from aquaculture production units and in turn, in the production of biomass plant for feeding terrestrial and aquatic organisms. This research aims to evaluate the efficiency of duckweeds Spirodela sp. and Lemna sp. in the treatment of tilapia effluents (Oreochromis niloticus). The experiment was performed in triplicate and was conducted under natural environmental conditions within the facilities of the Laboratory of Applied Aquaculture Research (LAAR) of the Technological Institute of Boca del Río (ITBOCA). Each treatment contained 230 liters of effluent in each tube, with a water column of 40 cm and a seeding density of 400 g/m2 of vegetative biomass. The evaluation of the efficiency of Spirodela sp. and Lemna sp., in the removal of dissolved nutrients, was performed in 7 monitoring times; 12, 24, 36, 48, 72, 96 and 120 h. The results showed an efficiency in nutrient removal at 120 h of 75, 74 and 66% of N-NH3; 96, 92 and 75% N-NO2; 93, 88 and 75% N-NO3; 75, 72 and 64% N-NTK; 73, 60 and 58% of N-org., and 73, 63 and 68% of P. On the other hand, the removal of TSS and BOD5, during the first 24 h, was 83, 54, 58% and 65, 59, 33%, in the treatments. The efficiency in nutrient removal of both duckweeds, showed that both plants can be used in the treatment of effluents, being a sustainable and economical alternative for the aquaculture industry.
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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 | 0.000 |
| 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 | 0.000 | 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".