Décontamination à l'échelle pilote de sols pollués en métaux toxiques par des procédés miniers et lixiviation chimique
Bibliographic record
Abstract
A pilot plant study permitted to demonstrate the scale up of a process using screening, spiral, hydrocyclone, and chemical leaching to decontaminate a soil. Thirty tons of a soil were treated. Screening allowed to separate a clean coarse fraction (>2 mm) of sand and gravel representing 70.9% of the total mass of soil. The <2 mm fraction of untreated soil contained 2200 mg Pb/kg, 350 mg Cu/kg, and 956 mg Zn/kg. The spiral treatment removed 10.2% Pb, 22.7% Cu, and 4.3% Zn, but it produced a small proportion of concentrate (0.53% of the whole soil). The spiral was more effective for Cu removal, because the 1–2 mm fraction of the soil contained more Cu than the smaller size fractions. The spirals are not usually efficient for the <75 µm fraction. This process was then not efficient for Pb removal, this metal being particularly concentrated in the <20 µm fraction. The <20 µm fraction, representing 6.48% of the total mass of soil and containing 5150 mg Pb/kg, was removed using a hydrocyclone. The chemical leaching process was used to treat 20.4% of the whole soil. This method has allowed to decrease the Pb concentration by approximately 57%, thus reducing the Pb content below the C criteria (1000 mg/kg) for commercial or industrial use in the province of Quebec. Overall, the process produced 91.1% of a noncontaminated soil and 8.9% of metallic residue and heavily contaminated soil. Key words: metals, soil, treatment, leaching, decontamination, mineral processing technology.
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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) | 0.001 | 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) | 0.002 | 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".