Effects of exogenous microbial inoculum on the structure and dynamics of bacterial communities in swine carcass composting
Bibliographic record
Abstract
Composting is a widely accepted method for the disposal of deceased livestock. It is a biological self-heating process during which animal carcasses are converted to fertilizer products. Additional inoculants can facilitate the composting progress. This study investigated how the addition of microbial inoculants could improve the composting effectiveness and could change the structure and dynamics of bacterial communities in the carcass composting process. Four strains of Bacillus were inoculated into the swine carcass composting piles. The groups with the additional inoculants showed a higher temperature in the thermophilic phase and higher germination indices in the composted products. The sequencing results showed that the dominant phyla were Proteobacteria, Firmicutes, and Actinobacteria, and the dominant classified genera were Brevibacterium and Bacillus. Canonical correlation analysis showed that temperature and moisture exerted a stronger influence on the bacterial community diversity. The interaction network of dominant genera and the abundance variation of the bacterial community demonstrated that the inoculated bacterial agent changed the structure of bacterial communities and enriched the diversity of the species due to antagonism and symbiosis among the dominant bacterial communities.
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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".