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Record W4399260999 · doi:10.5864/d2024-006

Climate change and drinking water: exploring resilience to wildfire in the BC Southern Interior

2024· article· en· W4399260999 on OpenAlexaffvenue
J. Ivor Norlin, Deni Olivares, Silvina C. Mema

Bibliographic record

VenueEnvironmental Health Review · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsInterior Health
Fundersnot available
KeywordsResilience (materials science)Climate changeGeographyEnvironmental sciencePhysical geographyEnvironmental resource managementOceanographyGeology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.959
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.004

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.

Opus teacher head0.041
GPT teacher head0.282
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

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