The management lessons learned from sediment remediation in the Detroit River – western Lake Erie watershed
Bibliographic record
Abstract
Abstract During the 1970s−1990s, considerable emphasis was placed on minimizing the inputs of polychlorinated biphenyls (PCBs) from active sources. In addition, between 1993 and 2001, ≈ $US130 × 10 6 was spent for sediment remediation within the western Lake Erie – Detroit River basin. In general, although PCB contamination of the Detroit River and Lake Erie declined significantly between the 1970s and mid‐1990s, it has remained fairly stable over the past 10 years. Control of PCBs and other contaminants at their source remains a primary imperative for action. Remediation of contaminated sediments is growing in importance, however, as greater levels of source control are achieved. From a sediment management perspective, it is estimated that between 1993 and 2001 a substantially higher mass of PCBs (over two orders of magnitude higher) was removed as a result of contaminated sediment remediation, as compared to navigational dredging of shipping channels. In addition, there is a strong and compelling rationale for moving expeditiously to remediate severely contaminated sediment while it is still relatively contained in a small geographical area. The cost of not acting in a timely manner might be to exacerbate environmental problems including increased deformities and reproductive problems in wildlife, delayed ecosystem recovery and increased costs, or even preclusion of future sediment remediation. Based on discussions at a United States of America–Canada workshop held in 2002, key management advice includes continued emphasis to be placed on remediating contaminated sediment hot spots (including evaluating the effectiveness of projects), integrated monitoring efforts to be focused on beneficial use restoration and a high priority to be placed on sustaining and building upon modelling efforts, in order to be able to accurately predict and evaluate ecosystem responses to remedial and preventive actions.
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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".