Assessment of the suitability of greensand filtration for Mn removal for private wells
Bibliographic record
Abstract
Greensand filtration is a common treatment option for manganese, sulfide, and iron removal; poor maintenance, however, can compromise effectiveness and even lead to increased post-treatment Mn concentrations. A private drinking-water well in exceedance of Health Canada’s maximum allowable concentration for Mn was sampled over a 1-year period to assess Mn removal via greensand filtration. Mn concentrations post-treatment were on average twofold higher than raw well water prior to media change. Reducing conditions, evidenced by negative oxidation–reduction potential (ORP) of the effluent, were central to generating soluble Mn(II). Monitoring revealed that water usage patterns caused variability in post-treatment Mn, with peak concentrations (>3.5× higher than influent water) observed after a 7-day idle period. As inadequate oxidant addition can lead to reducing conditions, monitoring ORP may facilitate proxy surveillance for Mn release. Findings underscore the importance of human factors (e.g., aesthetic concerns, barriers to maintenance, perceived risk) when evaluating overall benefits and drawbacks of treatment systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".