Hydrocarbon-degrading microbial populations in permanently cold deep-sea sediments in the NW Atlantic
Bibliographic record
Abstract
Permanently cold deep-sea sediments (2500–3500 m water depth) with and without indications of thermogenic hydrocarbon seepage were exposed to naphtha to examine the presence and potential of cold-adapted aerobic hydrocarbon-degrading microbial populations. Monitoring these microcosms for volatile hydrocarbons by GC–MS revealed sediments without in situ hydrocarbons responded more rapidly to naphtha amendment than hydrocarbon seep sediments overall, but seep sediments removed aromatic hydrocarbons benzene, toluene, ethylbenzene and xylene (BTEX) more readily. Naphtha-driven aerobic respiration was more evident in surface sediment (0–20 cmbsf) than deeper anoxic layers (>130 cmbsf) that responded less rapidly. In all cases, enrichment of Gammaproteobacteria included lineages of Oleispira, Pseudomonas, and Alteromonas known to be associated with marine oil spills. On the other hand, taxa known to be prevalent in situ and diagnostic for thermogenic hydrocarbon seepage in deep sea sediment, did not respond to naphtha amendment. This suggests a limited role for these prevalent seep-associated populations in the context of aerobic hydrocarbon biodegradation. • Natural hydrocarbon seepage does not precondition sediment for a faster response in oxic biodegradation microcosms. • Sediment exposed to low molecular weight hydrocarbons enriched bacterial taxa different from cold seep microbial signature. • Aerobic hydrocarbon-degrading populations were activated in deep anoxic sediments upon exposure to oxygen and 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".