Effect of nickel (II) and cobalt (II) mixture on aerobic sludge biomass
Bibliographic record
Abstract
Improper waste management is leading to heavy-metal contamination in domestic waste water, particularly in developing countries. This study examined the impacts of a 1:1 (w/w) mixture of nickel (Ni) and cobalt (Co) metal ions on reactor performance, metabolic activity and sludge biomass characteristics in sequential batch reactors (SBRs). The study also investigated the recovery potential of sludge biomass, when the metal ions were discontinued from the feed. Two sets of four identical SBRs labelled as RMix0 (control), RMix5, RMix25 and RMix75 were fed with a carbon (C) source and a mixture of nickel (II) + cobalt (II) metal ions in concentrations of 0, 5, 25 and 75 mg/l, respectively. The SBRs were operated with a cycle time of 12 h. The two phases consecutively investigated were the stressed phase (metals present in the feed) for 21 days and the recovery phase (metals absent in the feed) for 14 days. The results showed that during the stressed phase, chemical oxygen demand removal and metabolic activity in RMix25 and RMix75 deteriorated. Settling characteristics and biomass morphology underwent severe changes in RMix25 and RMix75. On the basis of the results, the study suggests monitoring the settling characteristics of biomass for early indication of metallic infiltration as a damage prevention strategy for sludge biomass in a waste-water-treatment plant.
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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 | 0.001 | 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".