A New Angle on Adaptive Management—Reducing Plausible Vulnerability in the Upper Great Lakes
Bibliographic record
Abstract
A new approach to identifying climate risks that would require adaptive management is being used on the Upper Great Lakes. The standard practice has been to simulate water related impacts expected under climate change by replacing current climate water supply with projected water supplies derived by downscaling the predicted changes in temperature and precipitation from General Circulation Models (GCMs) to transform the current hydrology. These simulations may provide a rough guess of what the future holds but are not particularly useful for decision making. Nor are they designed to be, although some decision is typically the endpoint of such efforts. For example, questions such as "when should we change our system" or "how much should we change" are difficult to address when no probability can be assigned to a particular climate change outcome used and there's no way to quantify the expected skill of GCM projections in the next century. The new approach presented here begins with stakeholders rather than climate models. Planners ask stakeholders and resource experts what water conditions they could cope with and which would require substantial policy or investment shifts. This is then formalized with a water resources systems model that relates changes in the physical climate conditions to performance metrics of interest to stakeholders. After these are established, hydrologists and climate scientists estimate the plausibility of the water conditions that exceed the coping thresholds, taking into account not only climate change but natural climate variability and stochastic variability observed with a stationary climate assumption. This paper reports on the early stages of a two year effort culminating in a Study Board recommendation to the International Joint Commission in the spring of 2012.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".