Low 13C-13C abundances in abiotic ethane
Bibliographic record
Abstract
Abstract Distinguishing biotic compounds from abiotic ones is important in resource geology, biogeochemistry, and the search for life in the universe. Stable isotopes have traditionally been used to discriminate the origins of organic materials, with particular focus on hydrocarbons. However, despite extensive efforts, unequivocal distinction of abiotic hydrocarbons remains challenging. Recent development of clumped-isotope analysis provides more robust information because it is independent of the stable isotopic composition of the starting material. Here, we report data from a 13C-13C clumped-isotope analysis of ethane and demonstrate that the abiotically-synthesized ethane shows distinctively low 13C-13C abundances compared to thermogenic ethane. A collision frequency model predicts the observed low 13C-13C abundances (anti-clumping) in ethane produced from methyl radical recombination. In contrast, thermogenic ethane presumably exhibits near stochastic 13C-13C distribution inherited from the biological precursor, which undergoes C-C bond cleavage/recombination during metabolism. Further, we find an exceptionally high 13C-13C signature in ethane remaining after microbial oxidation. In summary, the approach distinguishes between thermogenic, microbially altered, and abiotic hydrocarbons. The 13C-13C signature can provide an important step forward for discrimination of the origin of organic molecules on Earth and in extra-terrestrial 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 | 0.000 |
| 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) | 0.002 | 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".