Methane production potential of pulp mill sludges: microbial community and substrate constraints
Bibliographic record
Abstract
Sludges from pulp and paper mills represent a major ecological and environmental cost, and anaerobic digestion represents a method of waste reduction and energy recovery for these mills. This study compared methane production potential and microbial communities across 11 primary- and biosludges from five pulp and paper mills using various mill processes. We measured methane production from sludges in anaerobic batch reactor experiments over 64 days. Sludges were incubated with and without added substrate to test for organic substrate limitation versus inhibition of methanogens. Initial microbial communities and changes to community composition were determined using Illumina MiSeq for metabarcoding of bacterial and archaeal 16S rRNA genes. Mean methane production potential varied greatly between sludges (0.002-79 mL CH4 g-1 TS). Among primary sludges, kraft mill sludge produced more methane than other mill types. For these other mills, biosludge produced more methane than primary sludge, which had evidence of methanogen inhibition. Microbial communities and diversity were influenced by the initial community composition, and high methane production was only seen in sludges with high diversity. A number of sludges innately produced substantial methane and may be targets for further modelling and larger scale testing of anaerobic digestion.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".