Microbial population dynamics in enhanced biological phosphorus removing activated sludge systems
Bibliographic record
Abstract
In terms of wastewater treatment, the activated sludge process is probably the most important biotechnological process at present. Although a considerable amount of work has been done on system design and process engineering, some systems designed for enhanced biological phosphorus removal (EBPR) fail, thus necessitating chemical precipitation to meet effluent standards. Studies on the microbial ecology of activated sludge to optimize the process have received much attention. Culture-dependent, fluorescent antibody and molecular techniques, as well as polyacrylamide gel electrophoresis and community-level carbon source utilization methods have contributed to a better, but yet incomplete understanding of EBPR as well as other problems, such as bulking and foaming, which are microorganism related. Hence, microbial diversity and, more importantly, the function of populations in a specific community have not been elucidated, excepting in cases where a specific function can be attributed to a specific microbial population (e.g., nitrification). Most of our current knowledge regarding the microbiology of EBPR has been descriptive. However, as the methodology for studying microbial population dynamics improve, it will lead to a fundamental understanding of EBPR, bulking, foaming, etc., which could lead to major improvements in process design and operation. Key words: enhanced biological phosphorus removal, activated sludge, population dynamics, microenvironments, physicochemical properties, metabolic processes, modeling.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".