Potential of Ulva lactuca for municipal wastewater bioremediation and fly food
Bibliographic record
Abstract
ABSTRACT Macroalgae are considered a promising approach for wastewater treatment and could also ultimately provide an alternative animal food source in addition to a biofuel feedstock. Their large size and/or tendency to grow as dense floating mats or substrate-attached turfs lead to lower separation and drying costs than microalgae. In this study, the macroalgae species Ulva lactuca (U. lactuca) were used to investigate their capacity for treating municipal wastewaters, and the feasibility of using the harvested biomass as a feed for the fruit fly Drosophila melanogaster, an animal model for biological research. Results indicated that U. lactuca could successfully grow on three types of wastewaters studied with biomass productivities of 8.12–64.3 g·DW (dry weight)/(m 2 ·d). The secondary wastewater (SW) was demonstrated as the most effective wastewater medium for U. lactuca growth. However, both high nitrogen (92.5%–98.9%) and phosphorus (64.5%–88.6%) removal efficiencies were observed in all wastewaters, particularly in primary wastewater and SW, while the highest removal rates (N 24.7 ± 0.97 and P 0.69 ± 0.01 mg/(g·DW·d)) were obtained in centrate wastewater. Moreover, the addition of 20% washed U. lactuca into 80% standard fly food (w/w) led to an extended life span and stable body weights in flies while not for the food treatment with 20% unwashed U. lactuca. This study demonstrates an effective approach for the macroalgae-based treatment of municipal wastewater and the biomass for animal feed.
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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 | 0.000 |
| 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".