United States progress in remediating contaminated sediments in Great Lakes Areas of Concern
Bibliographic record
Abstract
Starting in 1985, comprehensive Remedial Action Plans were initiated to restore impaired beneficial uses in Great Lakes Areas of Concern. These plans were a catalyst for developing programs to remediate contaminated sediments. In 1987, the U.S. Environmental Protection Agency implemented the Assessment and Remediation of Contaminated Sediment Program to: measure contaminant concentrations in sediments and their potential effects on aquatic life; evaluate risks to wildlife and human health; and test technologies that might be used to clean up these contaminated sediments. In 2002, the U.S. Great Lakes Legacy Act was signed into law with the intent to remediate contaminated sediments at Great Lakes Areas of Concern. Before Great Lakes Legacy Act, only limited progress had been made in addressing contaminated sediments, a major, intractable issue impacting 9 of the 14 listed beneficial use impairments in Areas of Concern. Between 2004 and 2017, Great Lakes stakeholders have completed a total of 46 contaminated sediment remediation projects in U.S. Areas of Concern, resulting in the remediation of over 6.6 million m3 of contaminated sediments at a cost of over $1 billion. Although much has been accomplished, more contaminated sediment remediation must be undertaken to fully restore Areas of Concern. The Great Lakes Legacy Act and Great Lakes Restoration Initiative have been essential components for completing this important remediation and restoration work in Areas of Concern that is resulting in significant economic and environmental benefits.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".