Risk assessment of Ni, Cr, and Si release from alkaline minerals during enhanced weathering
Bibliographic record
Abstract
Abstract Calcium- and magnesium-rich alkaline silicate minerals, when applied to soil, can aid in carbon dioxide sequestration via enhanced weathering. The weathering of these silicate minerals is also associated with the release of heavy metals such as Ni and Cr, depending on the composition of the parent rock, and also labile Si. This paper critically analyses the risk associated with the release of Ni, Cr, and Si from alkaline silicate minerals as a result of enhanced weathering to evaluate its potential to be applied as a soil amendment. Based on the available data in the literature, this study evaluates the soil contamination level and quantifies the risk these elements pose to human health as well as the environment. To assess these potential threat levels, the geoaccumulation index was applied, along with the method recommended by the US Environmental Protection Agency for health risk assessment. The main findings of this study indicate the potential release of Ni, Cr, and Si to exceed the soil quality guideline value. The geochemical index suggests that the analyzed samples are in the class 0–3 and represents sites that lie between uncontaminated zones to highly contaminated zones. The hazard index value for Ni and Cr is greater than unity, which suggests that Ni and Cr release poses a non-carcinogenic risk. The probability of labile Si concentration in the soil to exceed the critical value is found to be 75%.
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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) | 0.000 | 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 | 0.001 | 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 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".