Aerobic degradation of crude oil by microorganisms in soils from four geographic regions of China
Bibliographic record
Abstract
A microcosm experiment was conducted for 112 d by spiking petroleum hydrocarbons into soils from four regions of China. Molecular analyses of soils from microcosms revealed changes in taxonomic diversity and oil catabolic genes of microbial communities. Degradation of total petroleum hydrocarbons (TPHs) in Sand from the Bohai Sea (SS) and Northeast China (NE) exhibited greater microbial mineralization than those of the Dagang Oilfield (DG) and Xiamen (XM). High-throughput sequencing and denaturing gradient gel electrophoresis (DGGE) profiles demonstrated an obvious reconstruction of the bacterial community in all soils. The dominant phylum of the XM with clay soil texture was Firmicutes instead of Proteobacteria in others (DG, SS, and NE) with silty or sandy soil texture. Abundances of alkane monooxygenase gene AlkB increased by 10- to 1000-fold, relative to initial values, and were positively correlated with rates of degradation of TPHs and n-alkanes C13-C30. Abundances of naphthalene dioxygenase gene Nah were positively correlated with degradation of naphthalene and total tricyclic PAHs. Redundancy analysis (RDA) showed that abiotic process derived from geographical heterogeneity was the primary effect on bioremediation of soils contaminated with oil. The optimization of abiotic and biotic factors should be the focus of future bioremediation of oil contaminated soil.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".