Implementation and Enforcement of the Clean Water Act: Report from a Special Master and Monitor
Bibliographic record
Abstract
In 1978, Judge John Feikens, U.S. District Court of the Eastern District of Michigan appointed a panel of three Special Masters to investigate complex wastewater rate issues that had arisen between the City of Detroit and seventy-seven suburban customers who received treatment of their wastewater at the Detroit Wastewater Treatment Plant. This paper presents key recommendations from the Special Masters that later were implemented through the court. In 1979, Judge Feikens appointed a Monitor to assist the court in responding to allegations by the regulatory agencies including both the U.S. EPA and the State of Michigan that the City of Detroit had failed to implement the Consent Degree approved by the court in 1977. In 1989, the court referred three new activities to the Monitor. One action focused upon the control of Combined Sewer Overflows (CSO) in the Rouge River, one of the Areas of Concern identified by the International Joint Commission for the Boundary Waters of the U.S. and Canada. A second action required development of an Industrial Waste Control Program for the Detroit Wastewater Treatment Plant to meet the regulatory requirements of both the state and federal agencies for pre-treatment of industrial wastes. The third action task focused upon the requirements needed to bring the Downriver Wastewater Treatment Plant in Wyandotte Michigan into compliance with the Clean Water Act. In 1999, the court directed the Monitor to chair a seven-member Committee to Investigate Violations of the DWSD's NPDES Permit for a time period from August 1997 through March 1999. The court provided critical leadership and direction in the process of bringing these two major wastewater treatment plants in Michigan into full compliance with the provisions of the Clean Water Act and the Clean Air Act.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".