Diversions from the Great Lakes: Out of the Watershed and in Contravention of the Compact
Bibliographic record
Abstract
Alarmingly, in the next fifty years the United States will face not just drought, but complete dissemination of readily accessible water resources in areas ranging from its breadbaskets to its commercial and financial epicenters. As these lakes, reservoirs, wells, and aquifers drain, the communities that depend upon them will seek alternative and further-reaching water sources into which they can dip their proverbial straws. The most alluring and perhaps the most vital of these sources are the Great Lakes. In recognition that such straws may descend and that “Future Diversions and Consumptive Uses of Basin Water resources have the potential to significantly impact the environment, economy and welfare of the Great Lakes–St. Lawrence River region,” the region surrounding the Great Lakes championed for effective protections of Great Lakes waters. The Great Lakes–St. Lawrence River Basin Water Resources Compact (the “Great Lakes Compact” or the “Compact”), signed into effect in 2008 by President George W. Bush after being adopted in all eight states that border the lakes, as well as the two Canadian provinces to the north, is a legally-binding, international water compact that attempts to manage withdrawals out of the Lakes by creating a blanket prohibition on diversions outside of the Great Lakes Basin.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".