Climate change and drinking water: exploring resilience to wildfire in the BC Southern Interior
Bibliographic record
Abstract
The increasing frequency and severity of wildfires threaten the safety and sustainability of community drinking water systems. There is a need to strengthen regulations and health protection programs to increase the resilience of drinking water systems and ensure access to clean, safe, and reliable drinking water in areas prone to wildfires. But what aspects of drinking water supply system infrastructure and operations are most important for resilience? A literature review was conducted on drinking water system resilience to wildfire. Themes identified were then shared in interviews with key informants from the BC Southern Interior to gain further insight into the most important elements for water systems impacted by wildfires. Those interviewed, and in particular those from First Nations communities, highlighted gaps in the current recommendations regarding the importance of proactive watershed management and protection for community water systems resilience. This paper identifies tangible changes in infrastructure and operations that drinking water suppliers can implement to reduce vulnerability to wildfire impacts. Water suppliers and regulators should commit to implementing these improvements and increase community water system resilience. Disclosure This work was supported by a Pacific Institute for Climate Solutions intern grant (#36170-50280) with in-kind support provided from Interior Health and the BC First Nations Health Authority.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".