The effect of feeding on microbiome and biogas composition in anaerobic CSTR
Bibliographic record
Abstract
Abstract his study investigates the performance of two Continuous Stirred Tank Reactors (CSTRs) with a focus on biogas yield, physicochemical parameters, and microbial dynamics. By integrating experimental observations with insights from recent literature, the research aims to elucidate the intricate relationships between reactor conditions, microbiome composition, and biogas production efficiency. Two substrates were used: a control substrate (SB1) and a protein-rich test substrate (SB2). The study monitored key parameters such as pH, Total Alkalinity of Carbonates (TAC), and volatile fatty acids (FOS), and analyzed the microbial communities using high-throughput sequencing. Results indicated significant temporal variations in pH, TAC, and nitrogen levels, with a declining FOS/TAC ratio. The introduction of SB2 led to increased biogas production and methane content, particularly at higher Hydraulic Retention Times (HRT). The study also high-lighted the role of specific microbial taxa in enhancing biogas quality. These findings contribute to the development of optimized strategies for sustainable biogas production and process control.
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) | 0.001 | 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".