Transferring lessons learned from use of an ecosystem approach to restore degraded areas of North American Great Lakes to the Arabian Gulf
Bibliographic record
Abstract
Canada and USA have over a 100-year history of international cooperation on Great Lakes management. For over 30 years, federal, state, and provincial governments have successfully used locally-defined ecosystem approaches to develop and implement remedial action plans to restore beneficial uses impairments in 43 Great Lakes Areas of Concern. Each remedial action plan identifies use impairments/causes, remedial/preventive actions to restore uses, and implementation responsibilities and timeframes. Areas of concern are removed from the list when all uses are restored. Long-term efforts are needed to ensure sustainability through adaptive management. Shared resources, like the Arabian Gulf and Great Lakes, require collaboration among boundary countries to achieve common goals. A network of regional technical and governmental representatives could be established under or affiliated with the Regional Organization of Protecting Marine Environment to promote cleanup and ecosystem-based management of degraded areas of the Sea. It would have to be flexible, not prescriptive, science-based, action-oriented, and result in well recognized benefits to all partner countries. Workshops or conferences could be convened to address ecosystem issues. This network must be value-added and build capacity, and could eventually lead to use of locally-defined ecosystem approaches to develop cleanup plans for degraded areas of the Gulf, similar to Great Lakes Areas of Concern. Gulf countries, through the Regional Organization of Protecting Marine Environment and with support of a network, could then come together every three-five years to report on progress, identify research needs, celebrate successes, and establish next steps. Gulf I, II, and III conferences are building blocks for such a network. A logical next step would be to engage the Regional Organization of Protecting Marine Environment in the establishment of a network for ecosystem-based management and co-sponsorship of a Gulf IV Conference or other forum. In the spirit of cooperative learning, further exchanges of both scientific research and management practices would be beneficial between the Great Lakes and the Gulf.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".