Effects of Granular Activated Carbon Amendment, Temperature, and Organic Loading Rate on Microbial Communities in Up-Flow Anaerobic Sludge Blanket Reactors
Bibliographic record
Abstract
Methane recovery in up-flow anaerobic sludge blanket (UASB) reactors performing anaerobic digestion (AD) can be improved with adjustments in operational factors such as temperature, organic loading rate, and the addition of granular activated carbon (GAC). This study aims to perform a multiple operational factor analysis for their impacts on UASB microbial communities. Data collected from seven continuously operated UASB reactors and batch tests were analyzed using a range of bioinformatics and statistical tools. Temperature and reactor types were the most important factors in microbial communities in UASB reactors, although the addition of GAC also had a statically significant impact. The positive and negative correlations between classified phylotypes and performance indicators were determined. It was noted that more phylotypes were positively correlated with hydrogenotrophic specific methanogenic activity (SMA) than acetoclastic SMA. The occurrence network of the overall microbial communities from samples amended with GAC was modularized into eight main groups (occupying 92% of the nodes). Seven modules containing both methanogens and syntrophs were identified as potential functional communities for AD. These modules were mainly regulated by reactor types and driven by different combinations of operational factors using co-occurrence network analysis and generalized joint attribute modeling. Overall, the paper bridged operational configurations and reactor performance with microbial community dynamics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".