Socio-Economics of Remediating Contaminated Sediment for the St. Clair River Area of Concern
Bibliographic record
Abstract
Abstract Of the total amount of contaminated sediment in the upper stretch of the St. Clair River, there are approximately 4500 cubic meters of sediment contaminated by mercury and organic chemicals that represent five of the remaining seven impaired uses in the St. Clair River Area of Concern. This material is the result of industrial pollution over a number of years in the Sarnia, Ontario, “Chemical Valley” and has most likely entered the St. Clair River via a surface drain passing through an industrial landfill. Scientific studies indicate that the contaminated material is constantly migrating downstream and has a potential for significant releases from large vessel traffic propeller wash or ice action. This paper examines two remediation options in the light of the potential benefits from a cleanup of the contaminated sediments. While not a typical benefit-cost analysis, this study explores the relationship between remediation and the social and economic benefits associated with “delisting” of the St. Clair River as an Area of Concern. The study has implications for other Areas of Concern that have sediment-related problems and require remedial action to meet the goals of the Great Lakes Water Quality Agreement.
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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".