Factors influencing public perception and use of municipal drinking water
Bibliographic record
Abstract
Despite more stringent regulations concerning drinking water quality in many countries, the public is increasingly concerned about the safety of municipal tap water. For this reason, acquiring a better understanding of consumer perception of tap water is an important issue for water authorities and utility managers. In this study, water consumption choice and profile were investigated. The case under study is the territory of a water supply system in Québec City (Canada). Data on drinking water consumption was obtained through a questionnaire-based survey. Survey results showed that an important proportion (about one third) of the population under study do not drink tap water. To explain consumption choice (tap water or not) and consumption profile (levels of tap water consumption), binary and ordinal logistic regression analyses (LGA) were performed based on survey responses and complementary data resulting from measurements of water quality parameters in 32 locations throughout the water distribution system. Water quality information was managed through a water quality index (WQI). The WQI of each sampling point was associated with the location of each survey respondent using a geographical information system (GIS). LGA results showed that the geographical location of the consumer within the distribution system, the WQI and perceived risk toward water consumption were the main factors explaining both the water consumption choice and tap water consumption profile.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".