Use of collaborative funding to implement the Remedial Action Plan for the St. Louis River Area of Concern, Minnesota, USA
Bibliographic record
Abstract
In the late 1800s and early 1900s, collaborative funding by some of the nation’s leading tycoons led to development of Duluth, Minnesota and the surrounding area. This included Andrew Carnegie, Jay Cooke, Andrew Mellon, J. P. Morgan and John D. Rockefeller, to name a few. This tremendous period of growth and expansion included major industrial developments, including grain, lumber, iron mining, manufacturing, rail, shipping and shipbuilding, and associated development of the frontier. Although critical to the development of a vital community, these activities took a toll through unchecked alteration of natural habitat and contamination of St. Louis River estuary sediments. These adverse legacy impacts, which occurred well before the establishment of our current environmental regulatory framework, became well recognized in 1985 when the International Joint Commission’s Great Lakes Water Quality Board identified the St. Louis River as an Area of Concern requiring the development and implementation of a remedial action plan to restore all impaired beneficial uses. This commitment to Remedial Action Plans was then codified in the 1987 Protocol to the U.S.-Canada Great Lakes Water Quality Agreement. The initial Stage 1 Remedial Action Plans for the St Louis River Area of Concern was completed in 1992 with the initial Stage 2 Remedial Action Plan completed in 1995, followed by periodic updates through 2012. However, these updates did not have budgets and action timelines necessary to secure the financial commitments to implement identified actions. Establishment of the Great Lakes Restoration Initiative in 2010 led to the 2013 Remedial Action Plan update (i.e. Stage 2 Remedial Action Plan) that included a business plan that identified specific actions, timelines, and budget estimates. Minnesota began implementing recommendations utilizing collaborative funding through a partnership approach. Ninety-nine percent of the actions identified in the Remedial Action Plan have been completed or are currently underway. Funding needs and sources have been clearly identified and the project is on track to complete major actions by 2020 and remove all beneficial use impairments and delist as an Area of Concern, dependent upon confirmation of use restoration, by 2025.
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".