Data Driving Better Decisions In The Great Lakes-St. Lawrence River Basin
Bibliographic record
Abstract
The Great Lakes-St. Lawrence River Basin contains approximately twenty percent of the world’s surface freshwater. The five Great Lakes (Superior, Michigan, Huron, Erie, and Ontario) provide drinking water for 24 million people and support industry, agriculture, and a world-class fishery and ecosystem. The region includes parts of two nations, eight U.S. States and two Canadian Provinces, many Tribes and First Nations, thousands of municipalities and local governments, and a range of stakeholders. These various governments and partners work across borders to manage and protect the region’s water resources and share data and information through a legal and political framework that has evolved over several decades. The “Great Lakes Compact” and companion Agreement establish how each State and Province will enact laws to manage shared water resources, including water withdrawal criteria and water conservation and efficiency programs. This approach has earned national and international recognition for enabling the States and Provinces to achieve shared environmental goals within a flexible framework reflecting the region’s diverse history, geography, and political landscape. This approach has also lead to many policy innovations. Under the Compact and Agreement, the States and Provinces commit to gather and share comparable water use information to improve scientific understanding of the Basin’s waters, and to develop a collaborative strategy to strengthen the scientific basis for sound water management decision making. The States and Provinces report annual water use data for each lake watershed to a centralized, regional database. This data is used to improve decision making for water resource managers, policymakers, and water users through the development of annual water use reports, and compared against the Basin water budget in periodic cumulative impact assessments. The governance structure for this collaboration and its practical application to resource management serves as a model of cross-border data sharing and cooperation for other regions with shared water resources.
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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 | 0.010 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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; 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".