Enhanced pathogen removal in on-site biofiltration systems over organic filtration materials
Bibliographic record
Abstract
Municipal wastewater was treated in 4 biofilters packed with a mix of endemic tropical woodchips and natural fibers to evaluate the removal efficiency of organic matter and pathogen microorganisms under tropical conditions. Biofilters were operated during 400 days, with a hydraulic rate of 0.3 m3/m2.d and an aeration rate of 0.68 m3air/m2 h-1. Raw municipal wastewater presented higher concentrations, of organic matter and pathogens, than those reported for municipal wastewaters in temperate countries. However, pollutants were successfully removed: <98.5% of the organic matter as BOD5 < 99.99% of Faecal Coliforms (FC) and Total Colony Forming Units (TCFU), and < 96.93% Helminth eggs (HE) were removed remaining only very low concentrations in the treated effluent (≤2.5 mg DBO5/L; ≤ 240 FC/100 mL; ≤ 240 TCFU /100 mL and < 1.0 HE/5L). According with Mexican regulations (Nom 001-SEMARNAT, 1996) and with the EPA suggested guidelines for water reuse (U.S. EPA, 1992a) treated effluents with this quality can be safely reused for three main activities: Nonfood crop irrigation, landscape impoundments and for construction activities. The high removal efficiency of TCFU and FC may be related with a predatory activity of testate amoebas which were detected growing into the biofilters and, the most plausible hypothesis concerning HE removal is that they are retained by filtration over the organic materials.
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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 | 0.001 | 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".