Technical note: Further adjustments to the Rock-Eval® thermal analysis for soil organic and inorganic carbon quantification to avoid post-hoc corrections
Bibliographic record
Abstract
Abstract. Accurate quantifications of soil organic (SOC) and inorganic (SIC) carbon are essential for a better understanding of the global carbon cycle. The procedures usually used to quantify SOC and SIC (e.g., elemental analysis after pretreatment) rely on various approximations and can lead to analytical errors. Ramped thermal analyses are increasingly investigated to quantify SOC and SIC by heating a single aliquot and continuously measuring the carbonaceous compounds emitted. The Rock-Eval® thermal analysis (RE) has been standardized to estimate organic and inorganic C contents of oil-bearing rocks through two parameters named TOC and MINC, respectively. Moreover, its pyrolysis phase before the oxidation provides the basis for calculating indices to characterize soil organic matter (SOM). However, statistical post-hoc corrections of TOC and MINC are needed to adjust their estimations of SOC and SIC contents because SOC and SIC decomposition signals overlap at the end of the pyrolysis. A new cycle with a final pyrolysis temperature of 520 °C (PYRO520) instead of 650 °C is investigated to avoid SIC decomposition while preserving OM characterization during pyrolysis. The results are compared to the quantifications obtained with the standard analysis cycle (PYRO650) and by elemental analysis after pretreatments. The PYRO520 cycle corrects the misallocation of the end-of-pyrolysis signals between the TOC and MINC parameters and thus accurately and repeatably estimated SOC and SIC contents measured by EA after pretreatments without needing post-hoc corrections. Moreover, the values and interpretations of the indices characterizing SOM are not drastically modified by the pyrolysis modification.
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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 | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.102 | 0.097 |
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".