Metagenomic Analysis of the Bioremediation of Diesel-Contaminated Canadian High Arctic Soils
Bibliographic record
Abstract
As human activity in the Arctic increases, so does the risk of hydrocarbon pollution events. On site bioremediation of contaminated soil is the only feasible clean up solution in these remote areas, but degradation rates vary widely between bioremediation treatments. Most previous studies have focused on the feasibility of on site clean-up and very little attention has been given to the microbial and functional communities involved and their ecology. Here, we ask the question: which microorganisms and functional genes are abundant and active during hydrocarbon degradation at cold temperature? To answer this question, we sequenced the soil metagenome of an ongoing bioremediation project in Alert, Canada through a time course. We also used reverse-transcriptase real-time PCR (RT-qPCR) to quantify the expression of several hydrocarbon-degrading genes. Pseudomonas species appeared as the most abundant organisms in Alert soils right after contamination with diesel and excavation (t = 0) and one month after the start of the bioremediation treatment (t = 1m), when degradation rates were at their highest, but decreased after one year (t = 1y), when residual soil hydrocarbons were almost depleted. This trend was also reflected in hydrocarbon degrading genes, which were mainly affiliated with Gammaproteobacteria at t = 0 and t = 1m and with Alphaproteobacteria and Actinobacteria at t = 1y. RT-qPCR assays confirmed that Pseudomonas and Rhodococcus species actively expressed hydrocarbon degradation genes in Arctic biopile soils. Taken together, these results indicated that biopile treatment leads to major shifts in soil microbial communities, favoring aerobic bacteria that can degrade hydrocarbons.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".