Long-term field studies in Canada on monitoring pedogenic carbonate formation in agricultural soils via enhanced weathering of wollastonite: status and latest findings
Bibliographic record
Abstract
Among the several methods that have been proposed for mitigating carbon concentration in the atmosphere, enhanced rock weathering is regarded as a low-cost, low-energy and readily scalable approach that can store atmospheric CO2 for up to thousands of years through converting alkaline earth metals into stable carbonates. Application of silicate-rich minerals (e.g., wollastonite, basalt and olivine) has been found effective for capturing atmospheric carbon in different terrestrial mediums, including agricultural and urban soils. In Ontario, Canada, we have been performing long-term research on pedogenic carbonate formation in agricultural soils amended with crushed wollastonite/dolomite rock mined in Ontario. The mineral has been applied to the topsoil of a number of experimental and farming fields, and shallow soil samples are periodically collected at different depths (including 0-15 cm, 15-30 cm, 30-60 cm, and 60-100 cm profiles) from these plots in order to estimate the rate and amount sequestrated carbon, and its migration across soil/sub-soil horizons over several years. These experiments are part of our effort to develop analytical and modeling toolboxes for verifying soil inorganic carbon sequestration, in view of qualifying this practice for carbon credits. Such toolboxes can become valuable for private and governmental entities in contributing to meet emissions reduction goals, and in encouraging the adoption of ERW as a reliable and verifiable negative emissions technology. This presentation will present the status of the field trials and toolbox development, and our latest findings and research directions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".