A Decision-Analytic Approach to Managing Climate Risks: Application to the Upper Great Lakes1
Bibliographic record
Abstract
Brown, Casey, William Werick, Wendy Leger, and David Fay, 2011. A Decision-Analytic Approach to Managing Climate Risks: Application to the Upper Great Lakes. Journal of the American Water Resources Association (JAWRA) 47(3):524-534. DOI: 10.1111/j.1752-1688.2011.00552.x Abstract: In this paper, we present a risk analysis and management process designed for use in water resources planning and management under climate change. The process incorporates climate information through a method called decision-scaling, whereby information related to climate projections is tailored for use in a decision-analytic framework. The climate risk management process begins with the identification of vulnerabilities by asking stakeholders and resource experts what water conditions they could cope with and which would require substantial policy or investment shifts. The identified vulnerabilities and thresholds are formalized with a water resources systems model that relates changes in the physical climate conditions to the performance metrics corresponding to vulnerabilities. The irreducible uncertainty of climate change projections is addressed through a dynamic management plan embedded within an adaptive management process. Implementation of the process is described as applied in the ongoing International Upper Great Lakes Study.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".