Assessment of Calcimetry as a Reliable Method for Monitoring Soil Inorganic Carbon Stocks
Bibliographic record
Abstract
The reliable measurement of the inorganic carbon content of soils and its changes resulting from land management practices and amendments is crucial for precisely quantifying carbon stocks as part of monitoring, reporting, and verification schemes. While various methods are available for evaluating the carbonate content in soils, the most direct approach is calcimetry, which involves the dissolution of solid-phase carbonates and the evolution of gas-phase CO 2 through acid-initiated reactions. Despite being a well-established method, uncertainties about how reliable calcimetry is to measure small changes in soil inorganic carbon (SIC) or how its measurement may be affected by potentially interfering reactions, sample size, and solid–liquid contact call for a dedicated investigation of these effects. The present study demonstrates the reliability of the calcimetry method and its limits through a parametric analysis that investigated the effect of the solid-to-liquid ratio, the presence of unweathered silicate phases, and the presence of copious amounts of organic matter. The results point to the reliable performance of calcimetry within the range of soil conditions that can be expected to be encountered during activities involving enhanced rock weathering and other best management practices that aim to boost the global soil carbon stocks as a climate change mitigation strategy.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".