State of the StraitUse of Remote Sensing and GIS to Better Managethe Huron-Erie Corridor
Bibliographic record
Abstract
Understanding the Patterns and Successes of Our Huron-Erie Corridor The Huron-Erie Corridor, centered by Detroit, Michigan and Windsor, Ontario, is an exceptional example of a place with a concentration of various natural and human boundaries, contrasts, and complex dynamics. The inherent diversity and abundance of habitats in the Corridor in relation to the North American continent and the Great Lakes Basin gives it a centrality that has long attracted both wildlife and people. Of primary importance is the U.S.-Canada border which bisects the Corridor, oft en cited as the location of the largest flow of transnational trade on Earth. Here, a combination of geographical and historical factors intersected with access to abundant natural resources to allow the creation of one of the most heavily industrialized waterways ever to exist. Unfortunately, nature was degraded by intense urbanization and industrialization to the point that by 1970 the St. Clair, Clinton, Rouge, Ecorse, and Detroit rivers, and the River Raisin, were grossly polluted, and Lake Erie was overwhelmed by excessive phosphorus loadings, wastewater treatment plant effluent that was only receiving primary or minimal treatment, and persistent toxic substances. Since then, great efforts have been made by many to preserve and restore environmental integrity, bringing ecosystems back to a level of health perhaps unimaginable 40 years ago. Superimposed upon all this is the Detroit River International Wildlife Refuge – established in 2001 as the only international wildlife refuge in North America. This unprecedented international wildlife refuge represents a major opportunity to continue to restore and protect ecosystems in a socially, economically, and environmentally sustainable manner. While environmental conditions within the Huron-Erie Corridor and Detroit River International Wildlife Refuge have improved substantially, certain challenges remain to be dealt with. Ecological threats from over a hundred invasive species continue to mount, and urbanization, agriculture, industry, and global climate change present their own management concerns. Scarce economic resources make it increasingly difficult for governments and stakeholders to provide support for environmental concerns. This means that money and efforts directed towards environmental cleanup and ecosystem restoration must be managed and deployed efficiently and effectively, as well as monitored to determine the success of natural resource management practices.
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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 | 0.018 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".