Review of Mechanical and Hydraulic Dredging at Two Sediment Remediation Sites
Bibliographic record
Abstract
Tetra Tech assists the U.S. EPA Great Lakes National Program Office (GLNPO) with design and construction management of sediment remediation projects in the Great Lakes region. Tetra Tech is currently managing two sediment remediation projects for GLNPO: the West Branch of the Grand Calumet River (WBGCR) in Hammond, Indiana, and the Ottawa River in Toledo, Ohio.\nThe WBGCR flows through an industrialized area in northwest Indiana. Mechanical excavation of 32,000 cubic yards of sediment was completed in August 2010. The design for additional sections of the river is underway with construction planned for December 2010.\nThe Ottawa River is part of the Maumee River Area of Concern in northwest Ohio. Hydraulic dredging of about 250,000 yards of sediment from the Ottawa River commenced in May 2010 and is scheduled to be completed in November 2010. The principal contaminant of concern is polychlorinated biphenyls at concentrations both above and below 50 ppm.\nThis paper will compare and contrast the methods for sediment dredging, focusing on the specific site conditions that influenced selection of each approach as well as lessons learned during construction activities at both locations. Factors influencing remedy selection include sediment physical and chemical characteristics, site hydraulics, and site access.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".