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Record W2797786454 · doi:10.1002/ieam.4032

Environmental dredging residual generation and management

2018· review· en· W2797786454 on OpenAlex

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueIntegrated Environmental Assessment and Management · 2018
Typereview
Languageen
FieldEnvironmental Science
TopicEnvironmental and Sediment Control
Canadian institutionsnot available
FundersEngineer Research and Development CenterU.S. Environmental Protection Agency
KeywordsDredgingEnvironmental scienceSedimentContaminationHarbourResidualResidual riskHydrology (agriculture)Environmental engineeringEngineeringGeologyOceanographyEcologyGeotechnical engineeringComputer science

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.987
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.018
GPT teacher head0.265
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it