Symmetry and solitude: Status and lessons learned from binational Areas of Concern
Bibliographic record
Abstract
Areas of Concern are geographically distinct areas within the waters of the Great Lakes that are contaminated to the extent that they were originally identified by the International Joint Commission’s Water Quality Board and later codified in the 1987 Great Lakes Water Quality Agreement as areas requiring remedial actions. Five of the 43 Areas of Concern are binational (Canada, USA), and are located on every river or connecting channel that drains a Great Lake. Implementing an ecosystem approach, as called for in the Agreement, presents unique challenges for binational Areas of Concern due to multiple jurisdictions and communities, and hence greater institutional, program and participatory complexity. Our review of progress in each of the binational Areas of Concern suggests that a binational and ecosystem-oriented approach is underway in the St. Marys, St. Clair and Detroit River Areas of Concern, while the Niagara River and St. Lawrence River Areas of Concern are proceeding on decidedly more independent domestic tracks. Our case study analysis of the Detroit River and St. Lawrence River Areas of Concern suggest that well developed and formal governance frameworks, the establishment of informal networks, and maintaining flexibility within a science-focused approach create conditions better suited to a binational, ecosystem-oriented means of remediation.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.024 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".