Case Study of a Multi-Stage Filtration System for Remote and Northern Communities in Canada
Bibliographic record
Abstract
A pilot multi-stage filtration system for water treatment was operated at two sites with phosphorous nutrient limited (C:N:P of 546:24:1 w/w) and nutrient rich (C:N:P of 6.3:1.6:1w/w) source waters.The system had two parallel treatment trains: Train 1 consisted of pre-ozonation, roughing filtration and slow sand filtration (SSF); and Train 2 consisted of pre-ozonation, roughing filtration and biofiltration (BF).Nutrient limited conditions exhibited lower DOC removals than nutrient rich conditions when ozone was present (9.3% vs 26% DOC removal, respectively).However, there was no difference in removal when no ozone was present (5.6% vs 6.4% DOC removal, respectively).A similar trend was seen with UVA254 removals (20% vs 45% with ozone, and 12% vs 13% without ozone, respectively).At the nutrient limited site, there was no overall difference between removals in the SSF and BF under conditions with and without ozone (p>0.05).At the nutrient rich site applied ozone resulted in a difference in removal between the SSF and BF trains (p<0.05), while there was no difference between the trains when no ozone was present (p>0.05).These findings highlight the importance of source water characterization and pilot testing when attempting to utilize the benefits of biofiltration for water treatment.iii 4.4 Conclusions ........
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".