Construction of a normal temperature straw-rotting microbial community and its character in degradation of rice straw.
Bibliographic record
Abstract
【Objective】 In order to investigate the degradation activity,optimal condition of secreted cellulase activity and compose,the microbial community with efficient cellulose degrading ability in 28℃ was studied. 【Method】 Microbial community came from rotted rice straw was enriched and directional domesticated by improved Mandels medium. The standard cellulase activity assays were used to determine cellulase activity,the fermented juice was analyzed by gas chromatography mass spectrometry (GC/MS) method,and denaturing gradient gel electrophoresis (DGGE) was used to identify the composition dynamic of the community. 【Result】 The results showed that the microbial community could degrade 39.6% of rice straw gross weight within five days. When the culture medium volume was 1/5 of the whole and pH at 6 on the 5 days culture,the CMC reached the highest of 14 IU·ml-1. During the rice straw degradation,more than ten kinds of products were detected using GCMS. DGGE detected the dynamic change of microbial community composition,and the microbial composition changed greatly in different periods. The phylogenetic tree derived from 16s rDNA sequence was found that the closest relatives belong to Clostridium sp.,Brevibacillus sp.,Bartonella sp.、Bacteroidetes sp. 【Conclusion】 This microbial community could accelerate rice straw rotting.
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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) | 0.000 | 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".