Conceptual Framework for Collaboratively Managing Coupled Human and Natural Systems under Climate Change Uncertainty
Bibliographic record
Abstract
<p class="1Body">A collaborative decision making (CDM) framework is developed for managing coupled human and natural systems (CHANS) over time when managers are uncertain about one or more drivers of system behavior. The framework incorporates six elements: (1) framing the problem; (2) selecting management objectives; (3) choosing scenarios for future changes in one or more drivers of system behavior; (4) formulating alternative management actions; (5) estimating the values of management objectives and determining their compliance with maximum or minimum acceptable levels; and (6) determining preferred management actions for each driver scenario and time period. Application of the framework is illustrated for a hypothetical case study that determines preferred management actions over time for a highway corridor through a hypothetical national park based on four management objectives: minimizing soil erosion and vegetative losses along hiking trails in the highway corridor; and minimizing traffic congestion on the highway and visitor congestion on hiking trails in the highway corridor. Uncertainty about future visitor use of the highway corridor is taken into account by specifying low, medium, and high visitor use scenarios for the corridor. Preferred management actions for each visitor use scenario within time periods are determined using the fuzzy Technique for Order Preference by Similarity of Ideal Solution to rank management actions for each visitor use scenario. The preferred management action across visitor use scenarios for each time period is determined by applying the minimax regret criterion to maximum loss indices for the preferred management actions for visitor use scenarios.</p>
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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 | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".