Unfolding ‘big’ problems of small water system performance: a qualitative study in British Columbia
Bibliographic record
Abstract
In this study, small water systems (SWSs) serve a population of less than 5,000. This paper includes responses to a qualitative questionnaire from 66 SWSs (33% out of 200 SWSs) that identify the major problems within these systems across British Columbia (BC). Focusing on four interrelated components of SWSs (water quality issues, treatment and disinfection, water quality monitoring and water governance challenges), the identified major problems include: insufficient water monitoring programs; inadequate treatment prior to disinfection; insufficient funds to build water infrastructure; high turbidity; iron and manganese in source water; microbial contamination, especially in distribution networks; and high disinfection by-product formation. Based on the findings, the authors recommend implementing efficient water policies suitable for SWSs and strengthening funding support from governments. Developing long-term plans for effective management of water resources, while building strong communication among relevant stakeholders (ie municipal managers, operators and consumers), can also play a significant role in ensuring safe drinking water supplies. The findings can help BC policymakers understand the major problems of SWSs and their root causes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.025 | 0.012 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".