Response of microbial community and catabolic genes to simulated petroleum hydrocarbon spills in soils/sediments from different geographic locations
Bibliographic record
Abstract
AIMS: Study the response of microbial communities and selected petroleum hydrocarbon (PH)-degrading genes on simulated PH spills in soils/sediments from different geographic locations. METHODS AND RESULTS: A microcosm experiment was conducted by spiking mixtures of petroleum hydrocarbons (PHs) to soils/sediments collected from four different regions of China, including the Dagang Oilfield (DG), Sand of Bohai Sea (SS), Northeast China (NE) and Xiamen (XM). Changes in bacterial community and the abundance of PH-degrading genes (alkB, nah and phe) were analysed by denaturing gradient electrophoresis (DGGE) and qPCR, respectively. Degradation of alkanes and PAHs in SS and NE materials were greater (P < 0·05) than those in DG and XM. Clay content was negatively correlated with the degradation of total alkanes by 112 days and PAHs by 56 days, while total organic carbon content was negatively correlated with initial degradation of total alkanes as well as PAHs. Abundances of alkB, nah and phe genes increased 10- to 100-fold and varied by soil type over the incubation period. DGGE fingerprints identified the dominance of α-, β- and γ-Proteobacteria (Gram -ve) and Actinobacteria (Gram +ve) bacteria associated with degradation of PHs in the materials studied. CONCLUSION: The geographic divergence resulting from the heterogeneity of physicochemical properties of soils/sediments appeared to influence the abundance of metabolic genes and community structure of microbes capable of degrading PHs. SIGNIFICANCE AND IMPACT OF THE STUDY: When developing practical in-situ bioremediation approaches for PHs contamination of soils/sediment, appropriate microbial community structures and the abundance of PH-degrading genes appear to be influenced by geographic location.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 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 teacher head, 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".