Vulnerability of municipal freshwater provisioning in a climate uncertain future: the case of Coral Harbour, Nunavut, Arctic Canada
Bibliographic record
Abstract
Climate change is likely to be an important factor affecting the ability of Arctic communities to continue supplying freshwater from single-source reservoirs; yet, infrastructure planning and assessment processes rarely take climate change into consideration for needs-based improvements. Here, we identify potential threats to the provisioning of freshwater by examining local water sources in the community of Coral Harbour, Arctic Canada. While we did not identify any concerns over water quality through the analysis of samples collected in 2021–2022, we did find that the current reservoir for the community is insufficient to continue provisioning water over a typical 20-year planning horizon. We also note that if anomalous climate conditions occur (e.g., extreme temperatures), the exhaustion of the annual water supply could occur faster than projected, causing a local water shortage until ice-off when replenishment is possible. Hydrometric data collected from Post River, the source used to replenish the reservoir, also highlight the response of river levels to both rainfall and dry periods, and thus qualitatively demonstrates the potential impacts of future episodic late-summer droughts on river water availability, which could affect resupply. These results highlight the need to include climate-based assessment in freshwater infrastructure assessment and planning processes in remote Arctic communities.
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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 | 0.002 |
| Science and technology studies | 0.016 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".