A Review on the Water Dimensions, Security, and Governance for Two Distinct Regions
Bibliographic record
Abstract
Non-arid region countries, including Canada, enjoy abundant water resources, while arid countries such as Qatar struggle to meet their water needs. However, climate change threats to water resources are similar for both climatic regions. Therefore, this article discusses water dimensions, security, and governance for these different regions, i.e., non-arid Canada and arid Qatar, that distinctly respond to their water-related challenges. Limitations of the article include lesser water-related literature availability for Qatar than for Canada. Canada’s water resources appear vulnerable to climate change as it is projected to face >0.6 °C above the global average of 1.6 °C for the 20th-century temperature. Qatar is extremely vulnerable to dust storms, and rising sea levels, with the maximum temperature approaching 50 °C during the summer, and flooding during the winter. The sustainable use of water resources needs to address social, economic, political, climate change, and environmental dimensions of water. Other than climate change impacts and high per capita consumption of water, Qatar faces challenges of a rise in population (~29 million as of now), acute shortage of freshwater from rainfall (~80 mm per annum), high evapotranspiration (~95% of the total rainfall), depletion of groundwater, and low agricultural productivity due to infertile lands and water scarcity, all leading to food insecurity. The sustainable use of water resources requires improved regulations for water governance and management. Comparisons of water sustainability issues, dimensions, security, and governance facilitate discussions to improve water governance structures for resource sustainability, food security, and climate change adaptability, and show how one country could learn from the experiences of the other.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".