Comparison of treatment efficacy and stability of microbial populations between raw and anaerobically treated liquid pig manure, using PCR–DGGE and 16S sequencing
Bibliographic record
Abstract
The effects of adding an adapted inoculum to liquid pig manure (LPM) prior to anaerobic digestion were evaluated by standard analytical methods. In parallel, the phylogenetic diversity of the microbial community of raw and anaerobically digested pig manure was studied by both denaturing gradient gel electrophoresis (DGGE) and sequence analysis of 16S rRNA fragments amplified by polymerase chain reaction. Gas production, volative fatty acid production, removal of soluble chemical oxygen demand, and removal of volatile soluble solids were measured on raw and on inoculated liquid pig manure subjected to anaerobic digestion. DGGE profiles of 16S rRNA genes were used to compare the major elements of the bacterial community composition in raw LPM with those present under various incubation conditions. Major bands were excised and sequenced to gain insight into the identities of the bacterial populations from LPM treated under different conditions. The results show that the addition of an adapted inoculum did not have a major impact on the conversion of pig manure into soluble organic matter and did not significantly change the microbial populations present during anaerobic digestion of LPM. Bacterial composition also indicated that Clostridium species are important constituents of the LPM community.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".