Combining Sequential Gaussian Simulation with Linear Regression to Develop Rehabilitation Strategies Using a Hydrometallurgical Process to Simultaneously Remove Metals, PCP, and PCDD/F from a Contaminated Soil
Bibliographic record
Abstract
In this study, a new approach to predicting the ability of a hydrometallurgical process to simultaneously remove metal(loid)s, pentachlorophenol (PCP), and polychlorodibenzodioxins and furans (PCDD/F) from contaminated soil is developed. The remediation process consisted of attrition and alkaline leaching steps applied for the coarse (> 0.250 mm) and fine (< 0.250 mm) fractions, respectively. First, a contaminant granulometric distribution-CGD model was established from granulo-chemical analyses performed on 5 selected sampling points collected from the contaminated site to estimate the levels of metallic and organic (PCP, PCDD/F) contamination in the coarse (> 0.250 mm) and fine (< 0.250 mm) fractions of the entire sample (24) and reduce the analytical costs. The accuracy of the CGD model for each contaminant in both fractions was then evaluated by cross-validation. The CGD model, sequential Gaussian simulation (SGS), and linear regression analyses were combined to predict the ability of the attrition and leaching processes applied to the coarse (> 0.250 mm) and fine (< 0.250 mm) soil fractions to simultaneously remove As, PCP, and PCDD/F from contaminated soil, respectively. The results showed that the attrition process could effectively remove the contaminants below the regulation standards to allow the industrial use of the rehabilitated site, as the coarse fraction represents an average proportion of 84 ± 2% of the total soil. However, the leaching process was ineffective in decontaminating the fine fraction (< 0.250 mm), which represented an average proportion of 14 ± 1% of the total soil. Based on these results, the most suitable strategy for this site can be established and a methodological reference for similar studies in risk assessment can be provided.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".