Characterization of marine microbial communities around an Arctic seabed hydrocarbon seep at Scott Inlet, Baffin Bay
Bibliographic record
Abstract
Seabed hydrocarbon seeps present natural laboratories for investigating responses of marine ecosystems to petroleum input. A hydrocarbon seep near Scott Inlet, Baffin Bay, was visited for in situ observations and sampling in the summer of 2018. Video evidence of an active hydrocarbon seep was confirmed by methane and hydrocarbon analysis of the overlying water column, which is 260 m at this site. Elevated methane concentrations in bottom water above and down current from the seep decreased to background seawater levels in the mid-water column >150 m above the seafloor. Seafloor microbial mats morphologically resembling sulfide-oxidizing bacteria surrounded areas of bubble ebullition. Calcareous tube worms, brittle stars, shrimp, sponges, sea stars, sea anemones, sea urchins, small fish and soft corals were observed near the seep, with soft corals showing evidence for hydrocarbon incorporation. Sediment microbial communities included putative methane-oxidizing Methyloprofundus, sulfate-reducing Desulfobulbaceae and sulfide-oxidizing Sulfurovum. A metabolic gene diagnostic for aerobic methanotrophs (pmoA) was detected in the sediment and bottom water above the seep epicentre and up to 5 km away. Both 16S rRNA gene and pmoA amplicon sequencing revealed that pelagic microbial communities oriented along the geologic basement rise associated with methane seepage (running SW to NE) differed from communities in off-axis water up to 5 km away. Relative abundances of aerobic methanotrophs and putative hydrocarbon-degrading bacteria were elevated in the bottom water down current from the seep. Detection of bacterial clades typically associated with hydrocarbon and methane oxidation highlights the importance of Arctic marine microbial communities in mitigating hydrocarbon emissions from natural geologic sources.
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 | 0.001 | 0.001 |
| 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".