Endotoxin release from biologically active bench-scale drinking water anthracite/sand filters
Bibliographic record
Abstract
Endotoxin release from biologically active dual-media (anthracite/sand) bench-scale drinking water filters was investigated.The biological filters were typically net producers of endotoxin during normal operation with mean concentrations increasing from 6 endotoxin units (EU)/mL to 16 EU/mL in filter influent and effluent, respectively.The filter operating condition resulting in the greatest release of endotoxin occurred upon shutdown and subsequent restart, with the highest observed filter effluent endotoxin concentration being 745 EU/mL in the first pore volume following the return of flow through the filter.Effluent samples collected following filter shutdowns were chlorinated to determine the effect of bacterial cell death on endotoxin release.Chlorination did not induce immediate 'dumping' of endotoxin, nor did holding the chlorinated samples for 5 d result in an increase in aqueous endotoxin concentration.In addition to endotoxin, measurable quantities of potassium were detected in the interstitial water in the biofilter during shutdown periods.In order to reduce potential risk to dialysis patients and humidifier users, it is recommended that, following even short biofilter shutdowns, filters should be immediately backwashed or, failing this, the first five pore volumes of effluent water be discharged to waste to allow endotoxin levels to return to pre-shutdown values.
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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 | 0.000 |
| 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 | 0.000 |
| 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".