Review of climate change and drinking water supply systems: employee perspectives and potential tools for adaptation
Bibliographic record
Abstract
Climate change (CC) causes extreme meteorological events such as floods, droughts, water scarcity, heat waves, wildfires, and extreme cold. CC may also damage public infrastructures such as drinking water systems (DWSs) and reduce water quality from source to tap. The objective of this review paper is to provide a critical analysis of (1) the impact of CC on the three components of the DWSs: source, treatment, and distribution; (2) the perspectives of DWS employees on CC impacts and adaptation solutions for maintaining water quality; and (3) decision support systems (DSSs) that could be used by DWS employees for CC adaptation. A bibliographic research was conducted using international databases and search engines. The search for scientific articles resulted in the selection of 5234 articles, 78 of which were analyzed in more detail for the elaboration of this scoping review. This review shows that the impacts of CC on water quality are significant, but DWS employees are not enthusiastic about developing adaption measures. This review also shows that DSSs can help DWS employees facilitate the CC adaptation process for the protection of source water, improve water treatment plant operations, and enhance routine management of water quality in the distribution system. Further research is needed to identify strategies that might increase the awareness of and interest in climate change impacts, adaptation, and the use of DSSs among DWS employees.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".