Environmental Dredging in the St. Lawrence River: A Case Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.006 | 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 it