A Study on the Improvement Directions of the Implementation System for Marine Contaminated Sediment Remediation Projects in Korea
Bibliographic record
Abstract
Marine contaminated sediments pose long-term risks to ecosystems, fisheries, and human health, requiring systematic national management. This study analyzes Korea’s remediation projects, focusing on their procedural and institutional structures. The analysis shows that current practices depend on fragmented criteria for site designation, exclusive reliance on state funding, dredging-centered methods without feasibility studies, and weak monitoring and performance evaluation. Comparative review of international cases provides useful insights: the U.S. CERCLA enforces the polluter-pays principle and risk-based reviews; Germany’s BBodSchG establishes shared liability for landowners and managers; Japan’s Minamata Bay remediation project combined strong governmental leadership with resident monitoring; and Canada’s Randle Reef project institutionalized joint financing and stakeholder participation. Based on these findings, this study suggests five key directions for Korea: (1) risk-based, multi-criteria site designation; (2) diversified financing with stakeholder participation; (3) feasibility-based technology selection beyond dredging; (4) KPI-driven monitoring and feedback; and (5) institutionalized governance and transparency. These reforms are expected to shift the current state-dominated system toward a more sustainable and participatory framework for marine sediment remediation projects.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".