Rice straw anaerobic digestion enhancement through bioaugmentation: effect on the microbial community
Bibliographic record
Abstract
Abstract The degradation of agricultural residues by anaerobic digestion and their bioconversion to methane is still hampered by the search for pretreatment strategies due to the lignocellulosic content that limits the efficiency of the process. Adding an enriched microbial consortium could be an alternative for the biological treatment of lignocellulosic biomass. During the degradation process, it is necessary to study the dynamics and structure of the microbial community. The objective of this study was to evaluate the addition of an enriched microbial consortium, and its effect on the methane-producing prokaryotic community during the anaerobic digestion of rice straw. The consortium was characterized by diversity, microbial community dynamics, and taxonomic identification. The rice straw anaerobic digestion was bioaugmented using the microbial consortium in 10 L semi-continuous stirred tank reactors (35 ± 2°C) for 70 days of operation at increasing organic loading rates up to 1.8 g VS L -1 d -1 . Relative to the control reactor, higher and more stable methane production was obtained with the biological treatment strategy. The metagenomic method allowed identification down to the genus and species level of microbial consortium and the prokaryotic community within the reactors. From the knowledge of the diversity and dynamics of the microbial community, possible preferential metabolic pathways were presumed. The enhanced anaerobic degradation of rice straw by the microbial consortium and its effect on the methane-producing microbial community demonstrated that it could be used as a bioproduct for the treatment of agricultural waste for energy purposes.
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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 | 0.000 |
| 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) | 0.001 | 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".