The science of ecosystem-based management on a global scale: The Laurentian Great Lakes, Lake Ontario, and the Bay of Quinte as a nested case study
Bibliographic record
Abstract
Lake ecosystems are our sentinels of environmental change and their effective management is one of our key planetary challenges in the 21st century. The evolution of ecosystem science as a basis for management is reviewed using the nested set of the Laurentian Great Lakes, Lake Ontario, and the Bay of Quinte as a primary focus. Other great lakes of the world, many of which are in Canada, provide a secondary focus. Ecosystem science has a long history in the Laurentian Great Lakes with developments driven in large part by the Great Lakes Water Quality Agreement, Lake-Wide Management Plans, and Remedial Action Plans for Areas of Concern. By comparison most other large Canadian lakes have received little attention as is the case with many of the world's great lakes. The substantial arsenal of tools and knowledge accumulated in the Great Lakes can serve as a model for other lake systems. As the range of ecosystem management problems has continued to grow, the motivating theme has shifted from restoration through rehabilitation to adaptation. The main challenge is to coalesce the many stresses we previously have sought to manage singly: land use, population growth, habitat degradation, resource exploitation, invasive species, pollutant and contaminant loadings, and, finally, climate change. Essential features of effective ecosystem-based management are: a whole system view, active adaptive management, acceptance of science-based evidence, and shared goals with common objectives. The last two may prove the greatest hurdle as society becomes ever more divided and fractious given the global onslaught of environmental and societal challenges. The Great Lakes experience shows there is hope.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".