Imaging of Ancient Microbial Biomarkers within Miocene Dolomite (Kuwait) Using Time-of-Flight Secondary Ion Mass Spectrometry
Bibliographic record
Abstract
Time-of-Flight–Secondary Ion Mass Spectrometry (ToF-SIMS) using a bismuth liquid metal ion source was utilized to characterize and image microbial biomarkers within dolomite from early-middle Miocene coastal mud volcano outcrops in Kuwait. ToF-SIMS analysis revealed biomarkers of ancient microbial consortia of sulfate reducers and methane oxidizers participating in the anaerobic oxidation of methane. The identified lipid biomarkers comprised 17α(H),21β(H)-Norhopane, Hop-17(21)-ene or Hop-22(29)-ene (diploptene), non-isoprenoidal dialkyl glycerol diethers (DAGEs), and Diacylglycerol esters (DGs). The ion µ-scale images of carbonate rocks showed two characteristic styles: (1) high signal intensity of dolomite, halite, and biomarkers, where the biomarkers demonstrate a distinctive co-localization pattern with both dolomite and halite; and (2) a lack of dolomite, halite low signal intensity, and an absence of biomarker co-localization patterns. Our results highlight three remarkable observations. Firstly, the concomitance of dolomite and halite suggests a common source of magnesium and sodium supply, likely from the hypersaline Al-Subiya sabkha. This emphasizes the importance of hypersaline seawater for dolomite formation. Secondly, microbial biomarkers correspond to methane- and sulfate-rich conditions under which dolomite was formed. Lastly, the high intensity of biomarker signals and their association with dolomite and halite indicate that the consortia involved in dolomite formation have preferences for high-salinity conditions. The three observations align with previous studies that have highlighted the importance of methane-sulfate redox, high salinity, and halophilic microbes for dolomite formation. This work is the first to acknowledge ancient microbial biomarkers within Miocene dolomite in Kuwait, which aims to broaden the understanding of the biogeochemical processes triggering dolomite formation in similar environments and ancient geologic settings.
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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) | 0.001 | 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".