Establishing Climate Change Resilience in the Great Lakes in Response to Flooding
Bibliographic record
Abstract
Over the past decade, both the average rainfall and the frequency of high precipitation storm events in the Great Lakes Basin have been steadily increasing as a consequence of climate change. In this same period, cities and communities along the coasts are experiencing record high water levels and severe flooding events (ECC Canada et al. 2018). When cities are unprepared for these floods, the safety of communities and the water quality of the Great Lakes are jeopardized. For example, coastal flooding increases runoff pollution and contaminates the freshwater resource that 40 million people rely on for drinking water (Lyandres and Welch 2012, Roth 2016). Since the Great Lakes are shared between two nations, the United States and Canada, the region is protected by several international treaties and national compacts, including the Great Lakes Water Quality Agreement (GLWQA) and the Great Lakes Restoration Initiative (GLRI). In order to increase climate change resiliency against flooding in the region, we recommend the United States Environmental Protection Agency (EPA) work with Environment and Climate Change Canada to relocate the GLRI under the GLWQA in order to guarantee consistent funding and protection efforts. We additionally recommend expansion of both agreements in their scope and long-term commitments to engender cooperative efforts to protect the Great Lakes against climate change.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".