Characterization of hydrocarbon degraders from Northwest Passage beach sediments and assessment of their ability for bioremediation
Bibliographic record
Abstract
Global warming-induced sea ice loss in the Canadian Northwest Passage (NWP) will result in more shipping traffic, increasing the risk of oil spills. Microorganisms inhabiting NWP beach sediments may degrade hydrocarbons, offering a potential bioremediation strategy. In this study, the characterization and genomic analyses of 22 hydrocarbon-biodegradative bacterial isolates revealed that they contained a diverse range of key alkane and aromatic hydrocarbon-degradative genes, as well as cold and salt tolerance genes indicating they are highly adapted to the extreme Arctic environment. Some isolates successfully degraded Ultra Low Sulfur Fuel Oil (ULSFO) at temperatures as low as −5 °C and high salinities (3%–10%). Three isolates were grown in liquid medium containing ULSFO as sole carbon source over 3 months and variation of hydrocarbon concentration was measured at three time points to determine their rate of hydrocarbon biodegradation. Our results demonstrate that two isolates ( Rhodococcus sp. R1B_2T and Pseudarthrobacter sp. R2D_1T) possess complete degradation pathways and can grow on alkane and aromatic components of ULSFO under Arctic conditions. Overall, these results demonstrate that diverse hydrocarbon-degrading microorganisms exist in the NWP beach sediments, offering a potential bioremediation strategy in the events of a marine fuel spill reaching the shores of the NWP.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.000 | 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".