Environmental dredging residual generation and management
Bibliographic record
Abstract
Abstract The presence and magnitude of sediment contamination remaining in a completed dredge area can often dictate the success of an environmental dredging project. The need to better understand and manage this remaining contamination, referred to as “postdredging residuals,” has increasingly been recognized by practitioners and investigators. Based on recent dredging projects with robust characterization programs, it is now understood that the residual contamination layer in the postdredging sediment comprises a mixture of contaminated sediments that originate from throughout the dredge cut. This mixture of contaminated sediments initially exhibits fluid mud properties that can contribute to sediment transport and contamination risk outside of the dredge area. This article reviews robust dredging residual evaluations recently performed in the United States and Canada, including the Hudson River, Lower Fox River, Ashtabula River, and Esquimalt Harbour, along with other projects. These data better inform the understanding of residuals generation, leading to improved models of dredging residual formation to inform remedy evaluation, selection, design, and implementation. Data from these projects confirm that the magnitude of dredging residuals is largely determined by site conditions, primarily in situ sediment fluidity or liquidity as measured by dry bulk density. While the generation of dredging residuals cannot be avoided, residuals can be successfully and efficiently managed through careful development and implementation of site-specific management plans. Integr Environ Assess Manag 2018;14:335–343. © 2018 The Authors. Integrated Environmental Assessment and Management Published by Wiley Periodicals, Inc. on behalf of Society of Environmental Toxicology & Chemistry (SETAC) Key Points Intensive monitoring of dredging residuals at high-profile environmental dredging sites including the Hudson River, New York, USA, and Lower Fox River, Wisconsin, USA, provided data that better inform our understanding of residuals generation. The magnitude of dredging residuals is largely determined by site conditions, primarily in situ sediment liquidity as measured by density. Whereas the generation of dredging residuals cannot be avoided, residuals can be successfully and efficiently managed through careful development and implementation of site-specific management plans.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".