Removal of Heavy Metals (Cu, Pb, and Zn) from Contaminated Sediment Using a Flotation Technique
Bibliographic record
Abstract
Abstract This paper investigates the effectiveness of froth flotation to remove copper and other heavy metals from dredged anoxic sediments (<250 μm) using a lab scale flotation cell with a selected anionic collector. A sediment fraction selected for flotation tests was potentially contaminated with a copper concentration of about 1763 mg/kg. The sequential extraction study of sediments showed that copper was mainly incorporated, between 70%–85% in the sulfide-organic fraction. The mineralogical analysis of sediment particle by scanning electron microscopy identified that chalcopyrite was the predominant copper sulfide form in the sediments. Flotation tests under controlled, predetermined experimental conditions showed selective flotation removal of copper (70.4%) among other metals with less mass recovery (froth mass).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| 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 teacher head, 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".