A Computer Simulation of Water Quality Change Due to Dredging of Heavy Metals Contaminated Sediments in the Old Harbour of Montreal
Bibliographic record
Abstract
Abstract Aquatic sediments accumulate heavy metals that are discharged into the environment. This study investigates possible water quality changes due to release of heavy metals such as Cd, Ni, Zn and Pb into the water column during dredging of anoxic sediments in the Old Harbour of Montreal. An environmental impact assessment of the sediment removal requires estimating the speciation of heavy metals in the water column with and without the solid phase. Chemical equilibria in the St. Lawrence River water are calculated using the program EQUILIB from the software FACT. Results show that the water is oversaturated with respect to CaMg(CO3)2 and Fe(OH)3. It is speculated that (ZnO)(Fe2O3) and (NiO)(Fe2O3) control the solubility of Zn and Ni in the water. The speciation of heavy metals in pore water of anoxic sediments is calculated, taking into account that the corresponding sulfides are solid phases and control their solubility. The impact of various scenarios on the area of concern is reviewed from an ecotoxicological perspective. Dredging might change the redox and acid-base conditions in the water column. Dredging can lead to an increase in the concentration of heavy metals in the water column and a change of metal speciation, but its impact will be less visible if the sediments contain high levels of calcium acting as a buffer.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".