DISTRIBUTION OF THE DOMINANT MICROBIAL COMMUNITIES IN MARINE SEDIMENTS CONTAINING HIGH CONCENTRATIONS OF GAS
Bibliographic record
Abstract
Methanogens are implicated in the production of methane that accumulates in marine sediments. However, the factors that control the distribution of the microbial communities that influence the presence of methane in these sediments are not well understood. Our objective is to determine the quantity, diversity, and distribution of microbial communities in the context of abiotic (e.g., grain size, presence/absence of hydrates) and geochemical (redox state, organic carbon content) properties in gas-rich marine sediments. To this aim, DNA was extracted from deep marine sediments (25-175 mbsf) cored from continental slope locations including offshore India and the Cascadia Margin. The sediments yielded low levels of DNA (0.3-1.5 ng/g of sediment), and bacterial DNA appeared to be more readily amplified than archaeal DNA. Preliminary analysis of a subset of these samples from India using PhyloChip technology indicated 200-800 distinct archaeal or bacterial taxa in each sample. The PhyloChip detected the presence of methanogens, sulfate reducers, sulfur oxidizers, and other metal reducers, as more prevalent taxa. Infrequently, cores from relatively shallow sediments from central Hydrate Ridge and northern Cascadia (offshore Vancouver Island), and from India’s eastern margin contained macroscopically visible,
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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) | 0.000 | 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".