Contaminated Sediment Remediation in the Laurentian Great Lakes: an Overview
Bibliographic record
Abstract
Abstract Sediment contaminated with metals, persistent organic pollutants, nutrients and oxygen consuming substances can be found in many areas throughout the Great Lakes. However, the highest levels of sediment-associated contaminants and some of the worst manifestations of their resultant problems are found in the urban-industrial harbours, embayments and river mouths. Management options may include source control and natural recovery, removal and containment in a confined disposal facility or upland containment cell, removal and treatment, and in situ capping or treatment. Over the past 13 years (as of January 2000), over $580 million (U.S. and Canadian dollars combined) has been spent on 38 remediation projects in 19 separate areas. Not only have substantial resources been spent on sediment remediation, but the rate of expenditure has increased in recent years. In addition, substantially greater resources have been spent on pollution prevention and control of contaminants at their source as a prerequisite to sediment remediation. While most of the remediation has taken place as a result of regulatory actions, some has been the result of cooperative partnerships, demonstration projects and unilateral voluntary actions. In the future, cooperative agreements may be expected to play a greater role in resolving contaminated sediment problems.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".