Environmental Dredging in the St. Lawrence River: A Case Study
Bibliographic record
Abstract
The St. Lawrence River (SLR) Sediment Removal Project was part of an ongoing site-wide CERCLA remediation program addressing polychlorinated biphenyl (PCB)-impacted sediments and soil at General Motor's (GM's) 270-acre manufacturing facility property and adjacent off-site areas located in Massena, New York. The Record of Decision (ROD) issued for this site specified a sediment PCB target cleanup goal of 1.0 part per million (ppm), to the extent technically practicable. The sediment removal was conducted in accordance with the ROD and a Unilateral Administrative Order (UAO) issued by the United States Environmental Protection Agency (USEPA). In order to meet the project's cleanup goal, approximately 18,000 cubic yards (cy) of sediment rock and debris were removed via hydraulic and mechanical dredging during the summer and fall of 1995, and a sediment cap was designed and installed to address an area where final PCB levels in the sediment remained above 10 ppm, even after excessive attempts. Annual monitoring and maintenance activities are currently being performed at the site to ensure the integrity of the sediment cap. The sediment removal portion of this program was completed with the effective cooperation and teamwork of GM, USEPA Region 2, the New York Department of Environmental Conservation (NYSDEC), the St. Regis Mohawk Tribe (SRMT), Environment Canada, Blasland, Bouck & Lee, Inc. (BBL), and Sevenson Environmental Services. This paper provides a comprehensive overview of many aspects of this extensive river-dredging project, including studies, project scoping, contracting, sediment removal, and environmental monitoring.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| 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 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".