Effects of copper on the composition and diversity of microbial communities in laboratory-scale swine manure composting
Bibliographic record
Abstract
treatments) on the bacterial communities during swine manure composting. The abundances of the bacteria were determined by quantitative PCR and their compositions were evaluated by high-throughput sequencing. The results showed that the abundance of bacteria was inhibited by the H treatment during days 7-35, and principal component analysis clearly separated the H treatment from the CK and L treatments. Actinobacteria, Firmicutes, and Proteobacteria were the dominant bacterial taxa, and a high copper concentration decreased the abundances of bacteria that degrade cellulose and lignin (e.g., class Bacilli and genus Truepera), especially in the mesophilic and thermophilic phases. Moreover, network analysis showed that copper might alter the co-occurrence patterns of bacterial communities by changing the properties of the networks and the keystone taxa, and increase the competition by increasing negative associations between bacteria during composting. Temperature, water-soluble carbohydrates, and copper significantly affected the variations in the bacterial community according to redundancy analysis. The copper content mainly contributed to the bacterial community in the thermophilic and cooling phases, where it had positive relationships with potentially pathogenic bacteria (e.g., Corynebacterium_1 and Acinetobacter).
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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".