Biodegradation of multiple microcystins and cylindrospermopsin in clarifier sludge and a drinking water source: Effects of particulate attached bacteria and phycocyanin
Bibliographic record
Abstract
The effects of particulate attached bacteria (PAB) and phycocyanin on the simultaneous biodegradation of a mixture of microcystin-LR, YR, LY, LW, LF and cylindrospermopsin (CYN) was assessed in clarifier sludge of a drinking water treatment plant (DWTP) and in a drinking water source. The biomass from lake water and clarifier sludge was able to degrade all microcystins (MCs) at initial concentrations of 10µgL(-1) with pseudo-first order reaction half-lives ranging from 2.3 to 8.8 days. CYN was degraded only in the sludge with a biodegradation rate of 1.0×10(-1)d(-1) and a half-life of 6.0 days. This is the first study reporting multiple MCs and CYN biodegradation in the coagulation-flocculation sludge of a DWTP. The removal of PAB from the lake water and the sludge prolonged the lag time substantially, such that no biodegradation of MCLY, LW and LF was observed within 24 days. Biodegradation rates were shown to increase in the presence of C-phycocyanin as a supplementary carbon source for indigenous bacteria, a cyanobacterial product that accompanies cyanotoxins during cyanobacteria blooms. MCs in mixtures degraded more slowly (or not at all) than if they were degraded individually, an important outcome as MCs in the environment are often present in mixtures. The results from this study showed that the majority of the bacterial biomass responsible for the biodegradation of cyanotoxins is associated with particles or biological flocs and there is a potential for extreme accumulation of cyanotoxins within the DWTP during a transient bloom.
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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 | 0.001 | 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".