Electrokinetic sedimentation of contaminated Welland River sediment
Bibliographic record
Abstract
An experimental study on electrokinetic sedimentation of contaminated sediment is conducted. The sediment dredged from the Welland River in Ontario is heavily contaminated by metals, oil, and grease as a result of industrial activities over past decades. Characterization of the sediment is presented, followed by descriptions of the sedimentation columns used in the experiments and testing procedure. The application of electrokinetics on the Welland River sediment suspensions significantly increases the free settling velocity (up to 110%) and final solid concentration (up to 33%) and reduces the coefficient of sedimentation (up to 111%) and overall sedimentation time (up to 50%). These changes are functions of the initial solid concentration and applied electric field intensity. Based on the evaluation of all influencing factors, it is concluded that a continuous dc current generates better performance than intermittent current and electrokinetic sedimentation is more effective than chemical coagulation. All other conditions being equal, the combined application of electrokinetics and FeCl3 generated the best dewatering effect. The electrokinetic treatment generates pH changes ranging from pH 6.0 at the anode to pH 9.4 at the cathode which will generate a negligible environmental impact, as most metal contaminants will not be released to water in this pH range.Key words: electrophoresis, electrokinetics, sedimentation, contaminated sediment, waste management.
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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 | 0.000 | 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) | 0.000 | 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".