Renegotiation of the 1987 Great Lakes Water Quality Agreement: From Confusion to Promise
Bibliographic record
Abstract
For nearly four decades, the Great Lakes regime has invoked the Great Lakes Water Quality Agreement as the mechanism for binational cooperation on programs and policies. Many advances in water quality have led to unquestionable improvements in ecosystem quality, habitat and biodiversity, and water infrastructure. Still, Great Lakes scientists have issued compelling evidence that the ecological health of the basin ecosystem is at significant risk. In 2012, the Agreement will be revised for the first time in 25 years. The degree of engagement in a future Agreement, including scope, issues of significant importance, governance and collaboration will hinge on a thorough analytical process, so far seemingly absent, coupled with real consultation, so far marginally evident. Renegotiating the Agreement to generate a revitalized and sustainable future mandates that science inform contemporary public policy, and that inclusive discourse and public engagement be integral through the process. Many of these steps are still absent, and the analysis presented here strongly suggests that the constituents of the Great Lakes regime voice their views critically, emphatically, and often. If the negotiators listen, we can collectively make the Lakes Great.
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 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 | 0.049 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.027 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 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".