MétaCan
Menu
Back to cohort
Record W2072886797 · doi:10.2166/washdev.2013.126

Drinking water quality for peri-urban residents in Phnom Penh, Cambodia

2013· article· en· W2072886797 on OpenAlexaff
Kevin V. Thomas, Edward A. McBean, Heather Murphy

Bibliographic record

VenueJournal of Water Sanitation and Hygiene for Development · 2013
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsEnvironmental scienceWater qualityVendorWater resource managementEnvironmental engineeringGroundwaterWater sourceHydrology (agriculture)BusinessEngineeringEcology

Abstract

fetched live from OpenAlex

Piped distribution systems are limited to major urban centres in Cambodia, leaving the residents of peri-urban communities to rely on a variety of surface, rain and groundwater sources for their drinking water supplies. This paper examines microbial water quality results from two of Phnom Penh's peri-urban communities, and describes relationships between water source and treatment type, study site and storage vessel, relative to water quality guidelines. Treating water by boiling was a common practice, although the majority of residents indicated using boiling times far greater than required, which may impact adoption rates. A statistical difference is described between boiled water by source type, with boiled shallow well water having elevated E. coli levels. The only household drinking water type that met WHO guidelines most of the time was boiled rain or tank (vendor) water (56%); boiled rain or tank (vendor) water stored in a kettle, bucket/cooler or bucket with spigot met guideline values 69, 43 and 60% of the time, respectively. The highest quality water is from boiled rain or tank (vendor) water taken directly from a kettle. The findings described provide some insight on how to prioritize water options for various uses.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.306
Threshold uncertainty score0.365

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.033
GPT teacher head0.304
Teacher spread0.270 · 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 designBench or experimental
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

Citations2
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Water Sanitation and Hygiene for DevelopmentSame topicChild Nutrition and Water AccessFrench-language works237,207