Analysis of microbial community diversity and physicochemical factors in pit mud of different ages based on high-throughput sequencing
Bibliographic record
Abstract
In this study, Illumina MiSeq/NovaSeq high-throughput sequencing technology was used to sequence the terminal DNA fragments of microbial communities in Wuliangye pit mud. The results showed that there were 5 dominant bacterial phyla and 13 dominant bacterial genera in the pit mud, which belonged to 4 phyla, mainly Firmicutes. There were 3 dominant fungal phyla and 5 dominant fungal genera in cellar mud, which belonged to 2 phyla and concentrated in Ascomycota. According to the statistical data, the low pH value cellar pool is more conducive to the enrichment of acid-resistant or acid-biased bacteria, which is the key to flavor formation. In addition, the components of ammonium nitrogen, available phosphorus and available potassium in pit mud need to be replenished in time. In addition, sampling time, fermentation time, temperature, and other external environments also have certain effects on microbial diversity and abundance in the pit. With the use of cellars, various types of microorganisms are constantly evolving to adapt to the environment inside the pits. The succession rule of microbe in pit mud was preliminarily revealed, which provided the basis for improving the quality and technical development of Wuliangye.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".