Thirty-five years of restoring Great Lakes Areas of Concern: Gradual progress, hopeful future
Bibliographic record
Abstract
In 1985, remedial action plan development was initiated to restore impaired beneficial uses in 42 Great Lakes Areas of Concern (AOCs). A 43rd AOC was designated in 1991. AOC restoration has not been easy as it requires networks focused on gathering stakeholders, coordinating efforts, and ensuring use restoration. As of 2019, seven AOCs were delisted, two were designated as Areas of Concern in Recovery, and 79 of 137 known use impairments in Canadian AOCs and 90 of 255 known use impairments in U.S. AOCs were eliminated. Between 1985 and 2019, a total of $22.78 billion U.S. was spent on restoring all AOCs. Pollution prevention investments should be viewed as spending to avoid future cleanups, and AOC restoration investments should be viewed as spending to help revitalize communities that has over a 3 to 1 return on investment. The pace of U.S. AOC restoration has accelerated under the Great Lakes Legacy Act (GLLA) and Great Lakes Restoration Initiative (GLRI). Sustained funding through U.S. programs like GLRI and GLLA and Canadian programs such as Canada-Ontario Agreement Respecting Great Lakes Water Quality and Ecosystem Health and the Great Lakes Protection Initiative is needed to restore all AOCs. Other major AOC program achievements include use of locally-designed ecosystem approaches, contaminated sediment remediation, habitat rehabilitation, controlling eutrophication, and advancing science. Key lessons learned include: ensure meaningful public participation; engage local leaders; establish a compelling vision; establish measurable targets; practice adaptive management; build partnerships; pursue collaborative financing; build a record of success; quantify benefits; and focus on life after delisting.
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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 | 0.014 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".