Bacterial community response in Juye mining area at the early stage of cracks
Bibliographic record
Abstract
Abstract Due to the sensitivity of microorganisms to the environment, microorganisms with strong tolerance in the early stage of coal mining collapse will gradually move to a dominant position, and plants can improve soil quality and provide important carbon sources for microorganisms. The soil characteristics and the response of soil bacteria in the early cracks during 15 ~ 20 days of mining were studied. Compared with non-cracked farmland group (C), soil bulk density in cracked farmland group (F) increased significantly in the early stage of coal mining,while porosity was on the contrary (p < 0.05). The mineral elements (except Ca and Na) in F were significantly lower than those in cracked abandoned land group (A).The abundance of the microbial community might be more closely related to crop planting, while the evenness of the microbial community was more affected by cracks. Coal mining cracks make Proteobacteria enrich significantly, while crop planting is conducive to the enrichment of RB41 and Pir4_lineage. Soil moisture content and AN were significantly negatively correlated with the relative abundance, while pH was significantly positively correlated with it. Planctomycetes and Bacteroidetes, which were significantly enriched in the non-crack area, were significantly positively correlated with AP, while Thaumarchaeot was significantly positively correlated with Eh. The study provided a basis for improving the low ecological environment damage mining technology.
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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".