Change in the water quality of industrial channels due to resuspension of sediments contaminated with heavy metals
Bibliographic record
Abstract
Industrial channels were used extensively during the two previous centuries and have become heavily contaminated. This study investigates possible water quality changes in the Lachine Canal (Montreal) due to the release of heavy metals (Cd, Ni, Zn and Pb) into the water column during resuspension of anoxic sediments subjected to potential remediation. This release can be initiated by dredging activities for sediment removal. Equilibrium in the water from the Lachine Canal has been calculated using the program EQUILIB from the software FACT. The speciation of heavy metals in the water column was calculated with and without a solid phase before and after possible dredging. Speciation of heavy metals in pore water of anoxic sediments has been calculated, taking into account that corresponding sulfides are the solid phases controlling their solubility. The concentration of heavy metals under anoxic conditions considered could decrease by 8 orders of magnitude. The impact of various scenarios in the area of concern was reviewed from an ecotoxicological perspective. Dredging can possibly change the redox and acid–base conditions in the water column. The impact of dredging will be less if sediments contain calcium. Dredging can lead to an increase in the concentration of heavy metals in the water column and a change of metal speciation.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".