Removal of microcystin-LR from spiked water using either activated carbon or anthracite as filter material
Bibliographic record
Abstract
The occurrence of cyanobacterial toxins (blue-green algae) in drinking water sources is a big concern for human health. Removal of microcystin-LR (MC-LR) from drinking water was evaluated at the laboratory pilot scale using either granular activated carbon (GAC) or powdered activated carbon (PAC) and compared with the treatment using anthracite as filter material. Virgin GAC was more effective at removing MC-LR (initial concentration ranging from 9 to 47 microg L(-1)) to reach the World Health Organization recommended level (1.0 microg L(-1)). When the GAC filter was colonized by bacteria, the filter became less effective at removing MC-LR owing to competitive reactions occurring between protein adsorption (released by bacteria) and MC-LR adsorption. Using PAC, the concentration of MC-LR decreased from 22 to 3 microg L(-1) (removal of 86% of MC-LR) by the addition of 100 mg PAC L(-1).
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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 | 0.000 |
| Research integrity | 0.001 | 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".