Hydrological Analysis of Municipal Source Water Availability in the Canadian Arctic Territory of Nunavut
Bibliographic record
Abstract
In the Canadian Arctic, the availability of sustainable drinking water supplies is threatened by pressures such as increasing populations, climate change, and the remote geographic nature of the communities. The objective of this study was to conduct a screening level vulnerability assessment of municipal drinking water supplies in the Canadian territory of Nunavut with consideration for climate change, population growth, and infrastructure changes. A hydrological analysis of primary drinking water supply watersheds was performed to evaluate the relative vulnerability level in 24 Nunavut communities. We used a water balance model to predict annual water yield from each watershed using historical and projected future climate data. Approximately 25% of the study communities were projected to experience high vulnerability to water shortages by 2070, defined as using greater than 40% of available water from their source watershed on an annual basis. A medium level of vulnerability (using 20% – 40% of annual available water) was determined for 8% of the study communities and a moderate level for 12% (using 10% – 20% of annual available water). A low vulnerability level to 2070 (using less than 10% of annual available water) was determined for 55% of the communities. The vulnerability level was primarily influenced by source watershed size. The results of this study could be used as a component of a proactive strategy to help address water security issues in Nunavut.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".