Mineral–Soil–Plant–Nutrient Synergisms of Enhanced Weathering for Agriculture: Short-Term Investigations Using Fast-Weathering Wollastonite Skarn
Bibliographic record
Abstract
) stands out. Not only does the use of wollastonite lead to rapid pedogenic carbonate formation in soils, it can be readily detected for verification of carbon sequestration, but its weathering within weeks to months influences soil chemistry and plant growth within the same crop cycle of its application. This enables a variety of short-term experimental agronomic studies to be conducted to demonstrate in an accelerated manner what could take years to be observed with more abundant but slower weathering silicates. This study presents the results of three studies that were conducted to investigate three distinct aspects of wollastonite skarn weathering in soils in the context of both agricultural and horticultural plants. The first study investigated the effect of a wide range of wollastonite skarn dosages in soil (1.5-10 wt.%) on the growth of green beans. The second study provides insights on the role of silicon (Si) release during silicate weathering on plant growth (soybeans and lettuce). The third study investigated the effect of wollastonite skarn on the growth of spring rye when added to soil alongside a nitrogen-based coated fertilizer. The results of these three studies provide further evidence that amending soil with crushed silicate rocks leads to climate-smart farming, resulting in inorganic carbon sequestration, as well as better plant growth in agricultural (soybean and spring rye) and horticultural (green bean and lettuce) crops. They also demonstrate the value of working with wollastonite skarn as a fast-weathering silicate rock to accelerate our understanding of the mineral-soil-plant-nutrient synergism of enhanced weathering.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".