The effect of long-term wastewater cross-connection on the biofilm of a simulated water distribution system
Bibliographic record
Abstract
The drinking water distribution system simulator (DSS) from the U.S. EPA was used to assess the effect of long-term wastewater cross-connection on the biofilm of a simulated water distribution system. Initial experimentation determined that at 0.3% wastewater to system volume per day for 90 d injected 0.1% every 8 h into the DSS; study organisms were consistently present in the discharge water sample with only slight aesthetic problems (increased turbidity). During the cross-connection, incoming tap water and wastewater, and system discharge water were monitored to ensure that the source of study organisms was only the wastewater cross-connection and that study organisms were present in the water column of the DSS. Following elimination of the cross-connection, samples showed that study organisms were removed from the water column within 24 h, which was the hydraulic retention time of the system. Increased numbers of heterotrophic organisms were detected in the system discharge following cross-connection. Increased heterotrophs were recovered for the system biofilm, and study organisms, except culturable heterotrophs, were not recovered beyond 24 h. Results indicate that the existing biofilm grew during the cross-connection, and may have inhibited growth of specific study organisms introduced via the cross-connection. Key words: biofilm, wastewater, drinking water, microorganisms, cross-connection.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".