Hydrocarbon biodegradation by Arctic sea-ice and sub-ice microbial communities during microcosm experiments, Northwest Passage (Nunavut, Canada)
Bibliographic record
Abstract
The increasing accessibility to navigation and offshore oil exploration brings risks of hydrocarbon releases in Arctic waters. Bioremediation of hydrocarbons is a promising mitigation strategy but challenges remain, particularly due to low microbial metabolic rates in cold, ice-covered seas. Hydrocarbon degradation potential of ice-associated microbes collected from the Northwest Passage was investigated. Microcosm incubations were run for 15 days at -1.7°C with and without oil to determine the effects of hydrocarbon exposure on microbial abundance, diversity and activity, and to estimate component-specific hydrocarbon loss. Diversity was assessed with automated ribosomal intergenic spacer analysis and Ion Torrent 16S rRNA gene sequencing. Bacterial activity was measured by (3)H-leucine uptake rates. After incubation, sub-ice and sea-ice communities degraded 94% and 48% of the initial hydrocarbons, respectively. Hydrocarbon exposure changed the composition of sea-ice and sub-ice communities; in sea-ice microcosms, Bacteroidetes (mainly Polaribacter) dominated whereas in sub-ice microcosms, the contribution of Epsilonproteobacteria increased, and that of Alphaproteobacteria and Bacteroidetes decreased. Sequencing data revealed a decline in diversity and increases in Colwellia and Moritella in oil-treated microcosms. Low concentration of dissolved organic matter (DOM) in sub-ice seawater may explain higher hydrocarbon degradation when compared to sea ice, where DOM was abundant and composed of labile exopolysaccharides.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".