Interjurisdictional water quantity issues in North America;s great lakes region
Bibliographic record
Abstract
North America's Great Lakes-St. Lawrence River system contains about 18 percent of the world's freshwater supply. Yet water deficiencies, apportionment of water among competing interests, concerns over falling lake levels, and water export are becoming issues in this seemingly water rich region. Shared by two countries, two Canadian provinces, eight U.S. States and about 40 million people, the Great Lakes-St. Lawrence River region has been an industrial heartland of North America for two centuries. A lack of jurisdictional concern for water quantity issues as a result of abundant water for historical uses such as hydropower, inland and overseas shipping, commercial and recreational fisheries, and water supply for large population centres is now beginning to change. Current thinking and the necessity for ecosystem management, conservation sustainability and sharing the waters of the world is leading to a range of resource use and jurisdictional issues for North America's Great Lakes-St. Lawrence River region as we begin the 21st Century. How Canadians and Americans deal on an interjurisdictional basis with climate change, water use and apportionment issues, water export, water levels and flows regulation, and water diversions will stretch the talents and abilities of these two countries to cooperate and resolve conflicts. The goal is to maintain and enhance the benefits that the Great Lakes bring to the region, to the two countries, and to the people that share this invaluable water resource.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".