Bacterial community succession in dairy manure composting with a static composting technique
Bibliographic record
Abstract
This study applied high-throughput sequencing technology and PICRUSt (phylogenetic investigation of communities by reconstruction of unobserved state) to examine the microbial population dynamics during the composting of dairy manure and rice straw in a static (without turning) composting system. The results showed that the composition of the bacterial community varied significantly during the composting process. The dominant phyla included Firmicutes, Proteobacteria, Bacteroidetes, Actinobacteria, and Chloroflexi. Biodiversity indices showed that bacterial community diversity had the peak value during the mesophilic phase. Redundancy analysis indicated that nitrogen was the most important factor in the distribution of genera during the composting process. Finally, the Pearson correlation results suggested that Thermomonospora and Thermopolyspora could be the biomarkers of the composting maturation phase. The metabolic characteristics of the bacterial communities were predicted by PICRUSt. The result showed that metabolism of amino acids, lipids, and most of the carbohydrates increased during the whole composting treatment. However, methane metabolism, carbon fixation pathways in prokaryotes, and nucleotide metabolism decreased after the thermophilic phase. The present study provides a better understanding for bacterial community composition and function succession in dairy manure composting.
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 | 0.000 |
| 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) | 0.000 | 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".