Reconnecting people to the Detroit River – A transboundary effort
Bibliographic record
Abstract
Abstract Windsor in Ontario, Canada and Detroit in Michigan, USA are Great Lakes border cities on the Detroit River that have a long history of water pollution. Public outcry over water pollution in the 1960s led to the enactment of environmental laws starting in the early 1970s. As these laws were implemented and water quality improved, citizens started calling for improved public access to the river, including establishing linked riverfront greenways. This paper presents a case study of greenway development in these border cities based on indicator reporting to comprehensively assess ecosystem health. Findings show that waterfront greenways were catalyzed by cleanup of the Detroit River. As greenway systems expanded on both sides of the border, greenway stakeholders began to envision cross-border greenway connections that would stimulate ecotourism, help encourage healthy lifestyles, and enhance quality of life in southwest Ontario and southeast Michigan. Recommended next steps include investing in greenway capacity building, identifying and testing creative financing options for greenways, formalizing institutional arrangements between Canada and the United States for a binational greenway network, and strengthening cross-border greenway connections by reestablishing a cross-border ferry, offering free access to the tunnel bus on weekends for cyclists, and hosting Windsor-Detroit open streets’ events. Robust transboundary greenway partnerships are critical to realizing the full potential of cross border greenway systems, including expanding outdoor recreation and ecotourism, conserving natural resources, and inspiring a stewardship ethic for shared ecosystems.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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; both teacher heads agree on what is shown here.
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".