Participatory water management modelling in the Athabasca River Basin
Bibliographic record
Abstract
Water is often used for a variety of conflicting purposes. Furthermore, as water is a dynamic resource, its equitable allocation across boundaries often poses problems for involved stakeholders. Integrated water resource management (IWRM) aims to promote the coordinated management of water across all boundaries. In theory IWRM is an effective solution to address multiple conflicting uses: however, in practice it is difficult to implement. This paper presents a case-study of an IWRM initiative in which the key component of participatory modelling is played out. Other important processes are integrated as well, such as problem structuring, social learning, and stakeholder engagement. In 2016-2017, approximately 30 stakeholders representing industry, municipalities, environmental NGOs, and federal/provincial government collaborated in order to explore opportunities to achieve sustainable watershed management in the Athabasca River Basin, Alberta Canada. Stress scenarios (including potential changes in climate, land use, and water use) were developed and used to test a series of water management strategies throughout the basin. These strategies were simulated within an integrated modelling tool in a live setting. Through this interactive process, promising strategies for sustainable water management were explored, and a series of recommendations for policy makers were identified. Recommendations include, but are not limited to, identifying areas for land conservation and reclamation priority, establishing in-stream flow need targets, and reducing water navigation limitations in the lower basin. Outlined through this paper, this case-study shows that examples of real-world participatory modelling efforts are in fact possible.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".