Contaminated Sediment Management: the Canadian Experience
Bibliographic record
Abstract
Abstract Since the beginning of North America's industrialization, the Great Lakes have been negatively impacted by the discharge of industrial, agricultural and municipal pollutants. The governments of Canada and the United States have recognized that the accumulation of pollutants within the bottom sediment and the water column has had a detrimental effect on the Great Lakes ecosystem. In 1972, Canada and the United States signed the Great Lakes Water Quality Agreement, which established common water quality objectives and commitments to programs and other measures to achieve these objectives. This included measures for the abatement and control of pollution from dredging activities. By 1985, the International Joint Commission, a body established by the two countries to provide advice on boundary water issues, identified 43 Areas of Concern where impaired water quality prevented full beneficial use of rivers, bays, harbours and ports. The Great Lakes Water Quality Agreement, amended in 1987, committed both countries to concentrate remediation efforts in these 43 Areas of Concern. This led to the development of Remedial Action Plans to assess and remediate contamination problems. Contaminated sediment was identified in all of these Areas of Concern. In 1989, the Canadian government created the 5-year $125-million Great Lakes Action Plan in support of the Great Lakes Water Quality Agreement. Of this, $55 million was allocated to the Great Lakes 2000 Cleanup Fund for the 17 Canadian Areas of Concern. A portion of the Cleanup Fund was designated for the development and demonstration of technologies for assessment, removal and treatment of contaminated sediment. Since its creation, the Remediation Technologies Program, established under the Cleanup Fund, has successfully performed 3 full-scale remediation projects, 11 pilot-scale technology demonstrations and 29 bench-scale tests. In addition to these projects, the program also evaluated existing sediment management practices and processes.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".