Great Lakes: EPA Needs to Define Organizational Responsibilities Better for Effective Oversight and Cleanup of Contaminated Areas
Bibliographic record
Abstract
Millions of people in the United States and Canada depend on the Great Lakes as a source for drinking water, recreation, and economic livelihood. Over time industrial, agricultural, and residential development on lands adjacent to the lakes have seriously degraded the lakes' water quality, posing threats to human health and the environment, and forcing restrictions on activities, such as swimming and fish consumption. In an effort to better protect the Great Lakes, and to address common water quality problems, the governments of the United States and Canada entered into the bilateral Great Lakes Water Quality Agreement in 1972. In 1978 the parties reached a new agreement, which, as amended in 1983 and 1987, expanded the scope of the activities by prescribing prevention and cleanup measures intended to immprove the lakes' conditions. Specifically, the 1987 amendment committed the two countries to cooperate with state and provincial governments to ensure, among other things, the development and implementation of remedial action plans (RAPs) for designated areas of concern (areas) located in the Great Lakes Basin areas contaminated for example with toxic substances known to cause deformities in fish or mammals. The countries have agreed to use RAPs for managing the cleanup process and restoring contaminated areas to their beneficial use, such as swimming or fishing. The countries have identified 43 contaminated areas: 26 located entirely within the United States, 12 in Canada, and 5 shared by both. The agreement obligates the International Joint Commission (IJC)-an international body charged with assisting the implementation of the agreement-to review the RAPs and provide comments on them.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".