Ammonia removal and nitrogen preferences evaluation of indigenous Malaysian microalga Halamphora sp. on white shrimp Penaeus vannamei wastewater
Bibliographic record
Abstract
Ammonia is ubiquitous in aquaculture systems and its removal is important for maintaining water quality and the health of the cultured animals. Microalgae are effective at removing ammonia from water, but the effectiveness of different microalgae species may vary. In this study, indigenous Malaysian microalgae isolated from shrimp ponds were screened for their ability in removing ammonia from synthetic culture media. The most efficient microalga’s nitrogen preferences and its growth and nitrogen removal in the early and late stages of shrimp culture wastewater were explored. It was found that four microalgae species namely Halamphora sp. BpSpD2, Chaetoceros sp. BpSpD3, Chlorella sp. BpSpG3 and Desertifilum sp. BpSpC1 were able to eradicate ammonia after 14 days of cultivation. Further investigation showed that Halamphora sp. BpSpD2 was able to remove 100 % ammonia within 5 days of culture. The nitrogen preferences of Halamphora sp. BpSpD2 indicated a preference for ammonia over nitrate as evidenced by the higher growth and removal efficiency of the treatments. Nitrogen removal efficiency of over 70 % was observed in treating 4 to 12 mg L-1 of TAN and nitrate. When tested in shrimp-cultured wastewater, Halamphora sp. showed a higher growth and 100% ammonia removal efficiency in the late stage of shrimp-culture wastewater. It also effectively removed 59% to 80% of nitrogen throughout both the early and late stages of shrimp culture wastewater. The results suggested that the microalga Halamphora sp. BpSpD2 has a significant potential to treat the effluent of an aquaculture system containing high concentrations of ammonia and nitrate.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 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".