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Record W2096315612 · doi:10.2166/wh.2011.087

Virus removal efficiency of Cambodian ceramic pot water purifiers

2011· article· en· W2096315612 on OpenAlexaff
Hamidreza Salsali, Edward A. McBean, Joseph H. Brunsting

Bibliographic record

VenueJournal of Water and Health · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicBacteriophages and microbial interactions
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsFiltration (mathematics)Environmental scienceSuspended solidsWater qualityPulp and paper industryCeramicWater treatmentEnvironmental engineeringPortable water purificationFilter (signal processing)Materials scienceMathematicsBiologyWastewaterMetallurgyEcologyEngineering

Abstract

fetched live from OpenAlex

Virus removal efficiency is described for three types of silver-impregnated, ceramic water filters (CWFs) produced in Cambodia. The tests were completed using freshly scrubbed filters and de-ionized (DI) water as an evaluation of the removal efficiency of the virus in isolation with no other interacting water quality variables. Removal efficiencies between 0.21 and 0.45 log are evidenced, which is significantly lower than results obtained in testing of similar filters by other investigators utilizing surface or rain water and a less frequent cleaning regime. Other experiments generally found virus removal efficiencies greater than 1.0 log. This difference may be because of the association of viruses with suspended solids, and subsequent removal of these solids during filtration. Variability in virus removal efficiencies between pots of the same manufacturer, and observed flow rates outside the manufacturer's specifications, suggest tighter quality control and consistency may be needed during production.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.028
GPT teacher head0.262
Teacher spread0.233 · 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 source (direct Gemma or distilled Codex), 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

Citations30
Published2011
Admission routes1
Has abstractyes

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