A modified Rock-Eval approach to track organics in (bio)carbonates
Bibliographic record
Abstract
Biominerals, notably carbonates, serve as valuable biosignatures for identifying past or present life in terrestrial environments. However, distinguishing between biogenic and abiotic minerals usually required multiple high-resolution techniques, challenging their application for the in situ search for extraterrestrial life in space missions with limited analytical capabilities. This study investigated the potential of gas profiles ( i.e. , carbon dioxide CO 2 , carbon monoxide CO, and sulfur dioxide SO 2 ) generated by Rock-Eval purified air combustion (in the range 50-700 °C) and dinitrogen pyrolysis (in the range 700-1000 °C) of 66 natural and laboratory carbonates to detect organics associated with these carbonate minerals that could hold clues to their origin (either abiotic or biogenic) and formation process. For bio-related and organo-carbonates containing Ca and Ca/Mg, CO and SO 2 emissions detected below 700 °C were the product of combustion and associated thermal cracking of organic compounds initially coating mineral grain surfaces, while those detected above 700 °C, during and after the thermal decomposition of Ca- and Ca/Mg-carbonates, were derived from the thermal cracking of organic compounds trapped within carbonate crystals, suggesting the carbonates formed in the presence of organic compounds of biological or abiotic origin. For hydrated Mg-carbonates, the interpretation is more challenging due to multiple phase transitions that overlap with the combustion and thermal cracking of surface and trapped organic compounds in the same temperature range. Overall, this study highlights the potential of our modified Rock-Eval approach as a valuable technique for rapidly identifying and characterizing potential biosignatures in Ca- and Ca/Mg-carbonate at the bulk sample scale, an approach that can be reasonably implemented on in situ space instruments for the search for present or past extraterrestrial life.
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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 | 0.001 |
| 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".