Tracking pedogenic carbonate formation and migration in agricultural soils amended with crushed wollastonite ore- Evidence from field trials
Bibliographic record
Abstract
Abstract Considered as a well-known carbon sequestration method, terrestrial enhanced rock weathering involves the application of crushed silicate-bearing minerals to urban and agricultural soils. Once dissolved in the soil-water system, alkaline minerals adjust the pH in a range favorable for pedogenic carbonate formation and, hence, atmospheric carbon drawdown. As a fast-weathering Ca-rich mineral, wollastonite is recognized as one of the primary candidates for this process. Although previous studies proved the potential of wollastonite to sequester carbon in croplands, no study has investigated the fate of wollastonite over the vertical profile of soil. Furthermore, no studies have investigated changes in the elemental composition of soils due to wollastonite amendment on a field scale. The present study presents the results of a multi-year sampling collection from different layers (0–15, 15–30, and 30–60 cm) of agricultural soil amended with wollastonite in Woodstock, Ontario, Canada. The impact of initial soil pH on pedogenic carbonate formation was also investigated with the inclusion of two more field trials. The results denoted wollastonite addition increases the inorganic carbon pool of soil up to 6.60 t CO2/ha at higher (20 t/ha) wollastonite dosage. The elemental composition and mineralogy analyses were indicative of weathering occurrence in soil samples. This study indicates that carbonate formation is not limited to surficial layers, and deeper layers also need to be taken into account for estimating carbon capture due to ERW practice.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".