An Interdisciplinary Water Risk Assessment Framework for Sustainable Water Management in Ontario, Canada
Bibliographic record
Abstract
Abstract The Province of Ontario in Canada illustrates contemporary water security issues, where despite perception of water abundance, water challenges arise locally. Water risks stem from biophysical dimensions of groundwater depletion, low surface water flows, and degraded quality, and, contextual dimensions of regulatory uncertainty, public concerns and perception. While academic, policy, and practitioner interest is growing, literature reveals major gaps in comprehensive assessment of multidimensional water risks at the subwatershed scale. Addressing these gaps, the study developed a locally attuned and interdisciplinary water risk assessment framework. Using secondary mixed data analysis, the study integrated quantitative and qualitative data for water quantity and quality risks, regulatory trends, water user conflicts for 38 subwatersheds in Ontario. The framework identifies subwatersheds and sectors at high, moderate, and low risk along with media and public concern themes. The study finds high and moderate risk potential in at least 50% of studied subwatersheds for all water risk indicators and challenges the myth of water abundance in Great Lakes watershed of Ontario. The study advances knowledge in water risk assessment by applying social‐ecological perspectives, interdisciplinary approaches of Risk Theory, and mixed methods to provide a comprehensive evaluation of water security and demonstrates integration of social science perspectives in the field of sociohydrology. Our framework assesses interdisciplinary water risks to inform multisector sustainable water management decisions. While spatially scoped to populous subwatersheds of Ontario, this framework can be methodologically generalized to other geographical regions by using local data.
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 | 0.004 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".