Constraining the Capacity of Global Croplands to CO<sub>2</sub> Drawdown via Mineral Weathering
Bibliographic record
Abstract
Terrestrial enhanced weathering of alkaline silicate minerals is a promising climate change mitigation strategy with the potential to limit the global temperature rise. The formation and accumulation of pedogenic carbonate and bicarbonate in soils/subsoils and groundwater offers a large sink for C storage; the amount of soil inorganic carbon (SIC) presently held within soils has been estimated to be 720–950 Gt of C. These values can be augmented by the addition of a variety of calcium and magnesium silicates via enhanced weathering. While the concept of the application of finely milled silicate rocks for faster weathering rates is well established, there has been limited discussion on the role of local climate, natural SIC content (i.e., the SIC innately present in the soil), and soil pH (among other important agronomic factors) on silicate weathering when applied to croplands, especially in view that the aim is to establish terrestrial enhanced weathering as a carbon dioxide removal (CDR) strategy on a global scale. In this work, we emphasized the importance of soil pH and soil temperature on silicate weathering and looked to estimate an upper limit of (i.e., constrain) the global capacity until the year 2100 for enhanced rock weathering (ERW) to draw down CO 2 in the form of accumulated pedogenic carbonate or soluble bicarbonate. We assessed the global spatial distribution of cropland soil pH, which serves as a proxy for local innate SIC; annual rate of pluvial (rainfall) precipitation; and soil temperature, and found that the potential CO 2 drawdown difference between faster and slower weathering silicates is narrower in Asia, Africa, and South America, while the gap is larger for Europe, North America, and Oceania.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".