Addressing Great Lakes coastal hazards through regional communities of practice
Bibliographic record
Abstract
Four regional Communities of Practice (CoPs) were developed across the coasts of Wisconsin and Minnesota to help communities connect with each other as they address their common challenges with coastal hazards like erosion, storms, and flooding. Great Lakes coastal hazards are significantly influenced by the water levels of the lakes, which can vary by up to ∼ 2 m (∼6.5 feet) between record highs and lows. These decadal fluctuations of water levels can lead to hazard impacts being forgotten in between the extremes, resulting in a diminished capacity to address coastal hazards when extreme conditions return. These hazards are expected to persist and potentially become more severe due to changing climate conditions. To build capacity to address coastal hazards, public officials and staff have consistently expressed a need for structure and leadership to guide knowledge sharing and collaborative action between coastal communities. The four regional CoPs coordinated learning and sharing among communities to collectively build coastal resilience and bring more resources to the region. These CoPs have provided members with learning opportunities, relationship building activities, technical assistance, and regular communications about hazards and resources. Members report that their participation in the CoPs resulted in outcomes that reduce coastal hazard risk, including improved planning, enhanced mapping capabilities, and identification of priority coastal management practices. The technical and social capacity created through the CoPs has helped members work across boundaries to navigate complex coastal hazard issues. Through regular evaluation, the CoPs have continued to evolve to meet changing needs of their member coastal communities.
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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 | 0.017 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".