Variability and limits of the unified membrane fouling index: application to the reduction of low-pressure membrane fouling by ozonation and biofiltration
Bibliographic record
Abstract
The impacts of ozone (0–8 mg O3 L−1) and filtration with biological activated carbon on membrane fouling by surface waters were investigated using three low-pressure membranes (two ceramics—UF or MF—and one polymeric—UF). The unified membrane fouling index (UMFI) was used to quantify the reversibility and irreversibility of membrane fouling. Minimum UMFI were calculated and repeatability assays were performed in order to evaluate the analytical detection limit and the precision of the method, respectively. The lowest ozone dose tested (1 mg O3 L−1) reduced the total fouling by 44, 63, and 41% for the polymeric membrane, the UF ceramic membrane, and the MF ceramic membrane, respectively. Further increase of the dose led to minor or no improvement, except for the ceramic MF membrane. For the ceramic membranes, a similar trend to that observed for total fouling was observed for hydraulically irreversible fouling. For the polymeric membrane, the hydraulically and chemically irreversible fouling were too low to be measured. Although biofiltration reduced the average dissolved organic carbon and turbidity by 25 and 50%, respectively, no significant fouling reduction was observed. The results indicate that irreversible fouling measurements are highly variable and most of the time below the analytical detection limit.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".