Domestic access to water in a decentralized truck-to-cistern system: a case study in the Northern Village of Kangiqsualujjuaq, Nunavik (Canada)
Bibliographic record
Abstract
ABSTRACT Municipal water supply through truck-to-cistern systems is common in northern Canada. Household satisfaction and concerns about water services likely impact user preferences and practices. This case study explores household perspectives and challenges with regard to domestic access to water in a decentralized truck-to-cistern system. A case study was conducted in the Northern Village of Kangiqsualujjuaq, Nunavik (Quebec, Canada). A paper-based questionnaire was completed by 65 households (one quarter of the population). Many households (37%) reported not drinking tap water from the truck-to-cistern system. Chlorine taste was a frequently reported concern, with those households being significantly less likely to drink water directly from the tap (p = 0.002). Similarly, households that reported a water shortage in the previous week (i.e., no water from the tap at least once) (33%) were more likely to express dissatisfaction with delivered water quantity (rs = 0.395, p = 0.004). Interestingly, 77% of households preferred using alternative drinking water sources for drinking purposes, such as public tap at the water treatment plant, natural sources or bottled water. The study underscores the importance of considering household perspectives to mitigate the risks associated with service disruptions and the use of alternative sources for drinking purposes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".