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Record W2096579462 · doi:10.4269/ajtmh.2008.79.407

Microbiological Effectiveness and Cost of Disinfecting Water by Boiling in Semi-urban India

2008· article· en· W2096579462 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAmerican Journal of Tropical Medicine and Hygiene · 2008
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsCentre for Global Health ResearchSt. Michael's Hospital
Fundersnot available
KeywordsBoilingFecal coliformEnvironmental scienceFecesPulp and paper industryMedicineWaste managementWater treatmentToxicologyEnvironmental healthEnvironmental engineeringChemistryBiologyWater qualityEcologyEngineering

Abstract

fetched live from OpenAlex

Despite shortcomings, boiling is the most common means of treating water at home and the benchmark against which emerging point-of-use water treatment approaches are measured. In a 5-month study, we assessed the microbiological effectiveness and cost of the practice among 218 self-reported boilers relying on unprotected water supplies. Boiling was associated with a 99% reduction in geometric mean fecal coliforms (FCs; P < 0.001). Despite high levels of fecal contamination in source water, 59.6% of stored drinking water samples from self-reported boilers met the World Health Organization standard for safe drinking water (0 FC/100 mL), and 5.7% were between 1 and 10 FC/100 mL. Nevertheless, 40.4% of stored drinking water samples were positive for FCs, with 25.1% exceeding 100 FC/100 mL. The estimated monthly fuel cost for boiling was INR 43.8 (US$0.88) for households using liquid petroleum gas and INR 34.7 (US$0.69) for households using wood.

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.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.273

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.011
GPT teacher head0.268
Teacher spread0.256 · 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