Comparison of microbial community structures in four Black soils along a climatic gradient in northeast China
Bibliographic record
Abstract
Mi, L., Wang, G., Jin, J., Sui, Y., Liu, J. and Liu, X. 2012. Comparison of microbial community structures in four Black soils along a climatic gradient in northeast China. Can. J. Soil Sci. 92: 543–549. Surveys of microorganisms across climatic gradients provide important information about their biodiversity and spatial distribution, which is linked to fundamental ecological functions. The present study investigated the bacterial communities, including total and culturable communities, and fungal communities in Black soils collected from Lishu (lat. 43°20′N, long. 124°28′E), Dehui (lat. 44°12′N, long. 125°33′E), Hailun (lat. 47°26′N, long. 126°38′E) and Beian (lat. 48°17′N, long. 127°15′E) in northeast China. Bacterial and fungal communities were evaluated by polymerase chain reaction (PCR)-denaturing gradient gel electrophoresis (DGGE) banding patterns of partial 16S rDNA and fungal rDNA internal transcribed spacer regions (ITS), respectively. Bacterial and fungal diversity, based on the number of DGGE bands, were similar among the locations, but cluster analysis of banding patterns showed distinct microbial communities along the climatic gradient. A closer relationship was found among soil bacterial (total and culturable) and fungal communities in neighboring locations than those at greater distance, which suggested that the spatial distribution of microbial community existed in the Black Soil Zone. Comparison of DGGE profiles among the four locations showed that the changes of fungal community and culturable bacterial community were greater than that of bacterial community, suggesting that fungal community and culturable bacterial community are more suitable to study microbial biogeographic distribution in Black soils.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".