Resilience and response of anaerobic digestion systems to short-term hydraulic loading shocks: Focusing on total and active microbial community dynamics
Bibliographic record
Abstract
Anaerobic digestion is known to be sensitive to operational changes, such as hydraulic loading shock, yet the impact on the microbiome, particularly the active RNA-based community, has not been fully understood. This study aimed to investigate the performance of anaerobic reactors and their microbial communities under short-term hydraulic loading shocks. Using synthetic wastewater, the reactor was subjected to 24-h shocks at three-fold and seven-fold the baseline loading rate, followed by DNA and RNA analyses to assess the system's resiliency and microbial responses. The research focused on shifts in major microbial groups and their functions, paying close attention to the active RNA community during loading shock events to better reflect the system's immediate condition. Findings indicated that although the microbial community structure, particularly among the archaea, was altered, the reactor quickly regained its balance. Differences were observed between DNA and RNA profiles and between regular and shock loadings; however, the alpha diversity and functions of the overall community were sustained. This study offers important insights for the design and operation of wastewater treatment plants, with the goal of achieving stable and efficient anaerobic digestion systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".