Co-Benefits of Wollastonite Weathering in Agriculture: CO<sub>2</sub> Sequestration and Promoted Plant Growth
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
To lock atmospheric CO2 at anthropogenic timescale, fast weathering silicates can be applied to soil to speed up natural CO2 sequestration via enhanced weathering. Agricultural lands offer large area for silicate application, but expected weathering rates as a function of soil and crop type, and potential impacts on the crops, are not well known. This study investigated the role of plants on enhanced weathering of wollastonite (CaSiO3) in soils. Using rooftop pot experiments with leguminous beans (Phaseolus vulgaris L.) and nonleguminous corn (Zea mays L.), CO2 sequestration was inferred from total inorganic carbon (TIC) accumulation in the soil and thermogravimetric analysis, and mineral weathering rate was inferred from alkalinity of soil porewater. Soil amendment with wollastonite promoted enhanced plant growth: beans showed a 177% greater dry biomass weight and corn showed a 59% greater plant height and a 90% greater dry biomass weight. Wollastonite-amended soil cultivated with beans showed a higher TIC accumulation of 0.606 ± 0.086%, as compared to that with corn (0.124 ± 0.053%). This demonstrates that using wollastonite as a soil amendment, along with legume cultivation, not only buffers the soil against acidification (due to microbial nitrogen fixation) but also sequesters carbon dioxide (12.04 kg of CO2/tonne soil/month, 9 times higher than the soil without wollastonite amendment).
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 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 it