The impacts of cyanobacteria on pulp-and-paper wastewater toxicity and biodegradation of wastewater contaminants
Bibliographic record
Abstract
This study investigated the effects of cyanobacteria from pulp-and-paper waste-treatment systems on biological toxicity removal and biodegradation of certain wastewater contaminants. In field and batch studies, using the Microtox assay, cyanobacterial biomass and final wastewater toxicity were significantly correlated. In softwood-based wastewater, a decrease in toxicity was negatively correlated with cyanobacterial biomass, but the correlation was positive in hardwood-based wastewater. In the softwood-based wastewater, toxicity remained higher in the light than it was in the dark, whereas in hardwood-based wastewater, toxicity was lower in the light than it was in the dark. All of these results were light-dependent, suggesting that the photosynthetic growth of cyanobacteria is required to induce significant effects. When grown in mixed cultures with bacterial degraders, cyanobacteria from pulp-and-paper waste-treatment systems generally impeded the biodegradation of the wastewater contaminants phenol and dichloroacetate (DCA). However, there was one case where the cyanobacterium Phormidium insigne improved the bacterial degradation of DCA. Doubling inorganic nutrient concentrations did not improve phenol or DCA biodegradation in the majority of cases, indicating that nutrient competition is not a major factor. These data suggest that cyanobacteria play an important role during the biological treatment of contaminants, and, hence, toxicity removal in pulp-and-paper waste-treatment systems.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".