Characterization of Physically Fractionated Wollastonite-Amended Agricultural Soils
Bibliographic record
Abstract
Wollastonite is a natural silicate mineral that can be used as an agricultural soil amendment. Once in the soil, this mineral undergoes weathering and carbonation reactions, and, under certain soil and field crop conditions, our previous work has shown that this practice leads to accumulation of inorganic carbon (calcium carbonate). Mineral carbonation is the carbon sequestration approach with the greatest potential for sequestration capacity and permanency. Agricultural lands offer vast areas onto which such minerals can be applied, while benefiting crops. This work illustrates a technique to separate wollastonite-containing soils into different fractions. These fractions are characterized separately to determine organic and inorganic content, as well as to determine the chemical and mineral composition. The aim is to detect the fate of wollastonite in agricultural soils, and the fate of weathering/carbonation products in the soil. The soils used in the study were collected from soybean and potato farmlands in Southern Ontario, and from an experimental pilot plot. Soil fractionation was done using sieving, and soil fractions were analyzed by a calcimeter, X-ray diffraction, and loss-on-ignition. Acid digested samples were measured by Inductively Coupled Plasma Mass Spectrometry. Carbonates and wollastonite were enriched by fractionation.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".