Empowering conventional Rock-Eval pyrolysis for organic matter characterization of the siderite-rich sediments of Lake Towuti (Indonesia) using End-Member Analysis
Bibliographic record
Abstract
Qualitative and quantitative changes of organic and carbonate carbon in sedimentary records are frequently used to reconstruct past environments, paleoproductivity and sediment provenance. Amongst the most commonly used proxies are Total Organic Carbon (TOC), Mineral Carbon (MinC), as well as Hydrogen (HI) and Oxygen Indices (OI) of organic matter (OM). Rock Eval pyrolysis enables the assessment of these quantitative and qualitative parameters with a single analysis. This is achieved through transient pyrolysis of the samples up to 650 °C followed by combustion up to 850 °C, with hydrocarbons, CO and CO 2 measured during the thermal decomposition of both OM and carbonate minerals. Carbonate minerals with low thermal cracking temperatures, such as siderite (<400 °C), can induce significant matrix effects which bias the TOC, MinC and OI Rock-Eval parameters. Here we assess the applicability of End-Member Analysis (EMA) as a means of correcting Rock-Eval thermograms for siderite matrix effects. For this, we performed Rock-Eval pyrolysis on sideritic sediments of Lake Towuti (Indonesia). New thermal boundaries were constrained in Rock-Eval thermograms using EMA to limit siderite matrix effects and improve TOC, MinC and OI calculations. Our approach allowed us to: (1) evaluate the influence of siderite matrix effects on Rock-Eval thermograms; (2) properly exploit a Rock-Eval dataset to characterize the type and sources of OM in siderite-rich sediments and (3) identify the OM behind degradation and mineralization processes. The Rock-Eval dataset revealed sediments with a substantial amount of refractory OM, especially in those where TOC is high and HI characteristic of autochthonous biomass. These results, associated to alternative indices used to assess OM preservation, suggest that refractory OM is residually enriched following strong degradation of labile compounds. Finally, relatively labile and refractory organic fractions may be consumed in the formation of siderite during this sequential process of OM mineralization.
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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".