Alkylotrophic Methanogenesis Forms Biogenic Methane with δ13C Values Comparable to Thermogenic Gas
Bibliographic record
Abstract
Summary Methane, a primary component of natural gas, is mainly derived from biogenic and thermogenic origins. Biogenic methane exhibits lower d13C-CH4 (<-55‰) due to microbial preferential utilization of 12C. However, secondary microbial methane (SMM), an important biogenic gas, was characterized to have d13C-CH4 values overlapping with thermogenic. This deviation cannot be adequately explained by conventional methanogenetic pathways, indicating alternative methanogenesis producing methane with less13C-depleted. Alkylotrophic methanogenesis differs from previous methanogenic metabolism and is widespread in oil reservoirs. Therefore, we incubated the enrichment culture of alkylotrophic methanogens with the eicosane and several crude oils as substrates (d13C-values from -35 to -27‰). The accumulated CH4 exhibited d13C values of -48‰ to -41‰, CO2 from -7‰ to +14‰. It exhibited smaller carbon isotopic fractionation than other pathways, ranging from -10 to -17‰, which can be attributed to its unique metabolic pathway. Since about 1/3 of methane derives from CO2 reduction, the only likely process with substantial fractionation, the overfall fractionation of alkylotrophic methanogenesis will be rather low. Its d13C value departs from canonical methanogenesis and plots in the interface between thermogenic and SMM. Given the prevalence of alkylotrophic methanogenesis in subsurface reservoirs, biogenic methane in reservoirs has been previously underestimated.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".