Formal Integration of Science and Management Systems Needed to Achieve Thriving and Prosperous Great Lakes
Bibliographic record
Abstract
For over a century, governments on both sides of the Canada–US border have employed diverse policy instruments and management tools to protect the Great Lakes. This crucial freshwater resource continues to show signs of degradation. We explore how the International Organization for Standardization Risk Management Standard (ISO 31000) can be used by governments to reduce the risk of failing to achieve the policy objectives of the Great Lakes. ISO 31000 facilitates the analysis of human activities that drive the causal pathways of ecosystem pressures–effects–impacts and analyzes the links between these causal pathways and the performance of management measures operating within the Great Lakes. ISO 31000 allows governments to shed light on why, despite best intentions, management measures are not working and enables governments to continually improve the management system until the risks of policy failures are reduced to acceptable levels, bringing new hope to the future of the Great Lakes.
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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 | 0.020 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.028 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".