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Record W4405325622 · doi:10.2166/wh.2024.314

Climate change and waterborne diseases in temperate regions: a systematic review

2024· review· en· W4405325622 on OpenAlexafffund
Eunice A. Salubi, Zemichael Gizaw, Corinne J. Schuster‐Wallace, Alain Pietroniro

Bibliographic record

VenueJournal of Water and Health · 2024
Typereview
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsGlobal Institute for Water SecurityUniversity of CalgaryUniversity of Saskatchewan
FundersGlobal Water Futures
KeywordsTemperate climateClimate changeWaterborne diseasesEnvironmental scienceGeographyEcologyBiologyWater quality

Abstract

fetched live from OpenAlex

Risk of waterborne diseases (WBDs) persists in temperate regions. The extent of influence of climate-related factors on the risk of specific WBDs in a changing climate and the projections of future climate scenarios on WBDs in temperate regions are unclear. A systematic review was conducted to identify specific waterborne pathogens and diseases prevalent in temperate region literature and transmission cycle associations with a changing climate. Projections of WBD risk based on future climate scenarios and models used to assess future disease risk were identified. Seventy-five peer-reviewed full-text articles for temperate regions published in the English language were included in this review after a search of Scopus and Web of Science databases from 2010 to 2023. Using thematic analysis, climate-related drivers impacting WBD risk were identified. Risk of WBDs was influenced mostly by weather (rainfall: 22% and heavy rainfall: 19%) across the majority of temperate regions and hydrological (streamflow: 50%) factors in Europe. Future climate scenarios suggest that WBD risk is likely to increase in temperate regions. Given the need to understand changes and potential feedback across fate, transport and exposure pathways, more studies should combine data-driven and process-based models to better assess future risks using model simulations

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.056
Threshold uncertainty score0.613

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
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)0.0000.000

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.

Opus teacher head0.204
GPT teacher head0.413
Teacher spread0.209 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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

Citations7
Published2024
Admission routes2
Has abstractyes

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