Exploring Collaborative Adaptive Management of Water Resources
Bibliographic record
Abstract
A host of new and “wicked” problems are plaguing today’s water resources and managers. The challenges and obstacles stemming from these problems are multidimensional, cumulative, and unprecedented and speak to the need for continuing to explore new approaches in water resources management and restoration efforts. This new class of interdependent problems is explored in this paper and some recently proposed ideas in collaborative adaptive management (CAM) are further developed to help address these types of “wicked” problems. It is argued that collaborative adaptive management, which combines the concepts of adaptive management and collaborative management, can help address the seemingly intractable technical, environmental and social problems inherent in complex social-ecological systems. Because it is important to highlight the importance of induction and emergent understanding under conditions of complexity, the concept of Ecological Policy Design is revisited as it relates to complex problem solving. Other concepts that are further explored and developed in this article include: project optimization that is based on devising composite solutions rather than attempting to “divide and conquer” individual subsystems; avoidance of “instability zones”; ecological and restoration efforts that are more “future responsive”; and the development of alternative hypotheses worthy of rapid prototyping through collaboration. Sustainable solutions are defined as those capable of governing and preserving the vitality of our waterways and the ecosystems that support them. To this end, we conclude that a more collaborative and adaptive approach to water management must be adopted if these types of solutions are to emerge.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 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".