ALGICIDAL EFFECT OF EXTRACTS FROM A GREEN MACROLAGAE (CHARA VULGARIS) ON THE GROWTH OF THE POTENTIALLY TOXIC CYANOBACTERIUM (MICROCYSTIS AERUGINOSA)
Bibliographic record
Abstract
The extracts of Chara vulgaris (a green macroalgae) were tested to explore its algicidal potential on Microcystis aeruginosa growth.Firstly, the anticyanobacterial effect of both macroalgae aqueous (MAA) and macroalgae etyl acetate (MEA) extracts against M. aeruginosa was assessed using both the paper disc diffusion and microdilution methods.Minimum inhibitory concentrations (MIC) and minimum algicidal concentrations (MAC) were evaluated.Secondly, the growth of M. aeruginosa in response to the MEA extracts was investigated in an experimental bioassay.To reveal the potential allelochemicals, total phenols (TPs), total flavonoids (TFs), tannins (TTs) were analyzed in both MAA and MEA extracts.The identification of the phenolic compounds in MEA extracts was performed by high-performance liquid chromatography (HPLC).The results from the bioassay demonstrated that MEA extracts inhibit the growth of M. aeruginosa in a concentration dependent way.The highest inhibition rate (IR) exceeds 83% on day (d) 4 of experimentation, and achieved (97.98%) on 7-d.HPLC analysis revealed seven phenolic compounds known as effective allelochemicals.Overall, the obtained results demonstrate that MEA extracts might be proposed as a potential allelochemicals, and it can be considered as an ecofriendly alternative algaecide to control Microcystis blooms in the eutrophic water bodies.
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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".