The Great Lakes Way: A Case Study of the Use of an Ecosystem Approach to Reconnect People to Natural Resources
Bibliographic record
Abstract
Canada and the United States have more than 40 years of experience in fostering the use of an ecosystem approach in restoring the Laurentian Great Lakes. Building on this foundation, the Community Foundation for Southeast Michigan and many partners have applied the same approach in creating The Great Lakes Way – an interconnected set of water trails and greenways stretching more than 240 km from southern Lake Huron to western Lake Erie. Major accomplishments of the first five years include: a consensus vision map, signed collaboration agreements from 45 communities and other trail stakeholders, an increase in the total number of usable and funded greenways from 167 km in 2020 (65 percent) to 199 km in 2024 (74 percent), a Memorandum of Understanding between U.S. and Canadian trail organizations to promote cross-border trail tourism once a new international bridge opens in 2025, and economic benefits estimated at $3.75-$5 billion annually. Lessons learned include: 1) bringing stakeholders together and developing a compelling vision; 2) building capacity; 3) identifying and empowering a boundary organization to bridge between distinct groups and facilitate communication and collaboration toward a common goal; 4) co-producing knowledge and co-innovating solutions; and 5) practicing adaptive management.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".