Bacterial anomalies in seabed sediments associated with deep water hydrocarbon seepage
Bibliographic record
Abstract
ABSTRACT Deep sea hydrocarbon seep detection relies predominantly on geochemical analyses of seabed marine sediment cores to identify the presence of gas or oil. The presence of seeping hydrocarbons in these locations alters resident microbial community structure, leading to culture-based biodegradation assays as a complement to geochemical tools for seep detection. Biodiversity surveys of microbial communities can offer a similar proxy for seeping hydrocarbons, but this strategy has not been extensively investigated in deep water settings. In this study, 16S rRNA gene sequencing of bacterial communities was performed on sediment cores obtained in >2500 m water depth at 43 different locations in the NW Atlantic Ocean. Core samples from as deep as 10 metres below seafloor (mbsf) were assessed for gas composition, gas isotopes and liquid hydrocarbons. Over 650 bacterial 16S rRNA gene amplicon libraries were constructed from different sediment depths at these locations. Select sites showed strong evidence for the presence of thermogenic or biogenic hydrocarbons such that bacterial population analyses revealed significant differences between hydrocarbon seep and non-seep locations. Specific bacterial indicators were associated with different sediment depth intervals. Caldatribacteriota and Campilobacterota OTUs were observed in high relative sequence abundance in hydrocarbon seep sediments, particularly in the 20-50 cmbsf interval. Furthermore, these groups were differentially abundant between sites with thermogenic and biogenic hydrocarbons. The patterns revealed here suggest that microbial screening has the potential to play a key role in hydrocarbon seep detection and characterisation in remote deep-sea environments.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".