Impact on water distribution system biofilm densities from reverse osmosis membrane treatment of supply water
Bibliographic record
Abstract
The quality of potable water is such that the concentration of nutrients available for growth of microorganisms within distribution systems is limited. In such systems carbon is often the growth limiting nutrient. Research conducted in the Netherlands has indicated that low levels (<10 μg/L) of available organic carbon in water is sufficient to maintain an actively growing population of heterotrophic, or organic carbon utilizing, bacteria in aquatic systems. However, the ability of commercially available and cost effective technologies to achieve such low concentrations of assimilable organic carbon in full-scale water systems is doubtful. Reverse osmosis (RO) systems have been used for many years to effectively remove contaminants from source waters. We challenged a water distribution system simulator (DSS) with water from a municipal system and water that was treated using an RO system under two concentrations of residual free chlorine to evaluate the effect of this disinfectant on biofilms in contact with low nutrient water. Our results showed that biofilm densities in the DSS carrying low nutrient RO treated water were lower than biofilm densities taken from the DSS when it carried water directly obtained from a municipal system.Key words: water distribution systems, reverse osmosis, biofilms, heterotrophic plate count, HPC, chlorine, assimilable organic carbon.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".