Assessment of Climate Resilience of Water Supply System in the Arctic Communities of Canada
Bibliographic record
Abstract
The Arctic region of Canada is especially vulnerable to the detrimental consequences of climate change, as projected winter temperatures in the north rise by approximately 3°C to 4°C over the following half-century. The purpose of this research work is to assess the climate resilience of water supply systems in Arctic communities of Canada in given a changing climate. This study investigates how the change in climatic conditions, mainly temperature, precipitation, and snowfall influence the reliability and sustainability of water infrastructures. Based on historical climate data from 1996 to 2023 and projections from a suite of climate models for future periods (2041–2100), the study indicates a rise in temperatures, shift in precipitation patterns, and changes in the variability of snow accumulation for water supply infrastructure. However, the analysis shows that water supply systems in Arctic communities are highly susceptible, primarily because the infrastructure components are exposed to severe climatic conditions. Preliminary findings suggest a frequency of extreme temperature events, both maximum and minimum poses a significant risk to the operational efficiency of water treatment and distribution systems. The study further reveals that the variation in the level of snow accumulation posed a high risk with a score of 49 on all the infrastructure components that were assessed in the year 2008 in the warmer season (spring and summer) which stressed the need for an immediate action to be implemented in managing the water supply system. Moreover, maximum temperature which was assess as a climate risk within the same period 2008 also exhibited a score of 42 and 49 for the water supply components which denotes that theses systems are under high risk therefore required an immediate action was needed to take place. This research contributes to the existing literature on climate resilience in the Arctic communities, highlighting how climatic changes interact in a complex way with water supply systems. Concisely, recommendations are proposed for policymakers, industry players, and stakeholders in the Arctic communities to prioritize climate resilience through policy integration, investing in advanced infrastructure technologies, strengthening community-based adaptation strategies, as well as to promote sustainable water management practices.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".