Wastewater Influent Microbial Immigration and Contribution to Resource Consumption in Activated Sludge Using Taxon-Specific Mass-Flow Immigration Model
Bibliographic record
Abstract
Abstract Wastewater influent microorganisms are part of the total chemical oxygen demand (COD) and affect the activated sludge (AS) microbial community. Precise modeling of AS processes requires accurate quantification of influent microorganisms, which is missing in many AS models (ASMs). In this study, influent microorganisms in COD unit were determined using a fast quantification method based on DNA yield and was compared with conventional respirometry method. The actively growing influent microorganisms were identified. A mass-flow immigration model was developed to quantify the influent-to-AS immigration efficiency ( m i ) of specific taxon i using mass balance and 16S rRNA gene high-throughput sequencing data. The modelled average m was 0.121-0.257 in site 1 (LaPrairie), and 0.050-0.126 in site 2 (Pincourt), which were corrected to 0.111-0.186 and 0.048-0.109 respectively using a constrain of m i ≤ 1. The model was further developed to calculate contributions to organic substrate consumption by specific taxa. Those genera with zero or negative net growth rates were not completely immigration dependent ( m i < 1) and contributed to 2.4% - 5.4% of the substrate consumption. These results suggest that influent microbiome may be important contributors to AS microbiome assembly and system performance (substrate consumption), which may help to improve future AS process modelling and design. Synopsis Influent microbial immigration lacks detailed taxon-specific quantification. This study presents quantitative methods and models for influent biomass, mass-flow immigration model, and resource consumption in activated sludge. Graphic Abstract
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".