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Record W2323004260 · doi:10.2166/washdev.2014.043

The potential of a semi-decentralised bulk water treatment approach for emergency relief

2014· article· en· W2323004260 on OpenAlexaff
Caetano C. Dorea, Vincent Jalaber

Bibliographic record

VenueJournal of Water Sanitation and Hygiene for Development · 2014
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsTurbidityEnvironmental scienceWater treatmentWater supplyWater qualityEnvironmental engineeringWater resource managementBusinessWaste managementEngineeringEcology

Abstract

fetched live from OpenAlex

Humanitarian water treatment interventions vary from package bulk ‘kits’ to household (point-of-use) options. Whereas the former can be perceived to be too complex to operate, the latter, whilst relatively simple and effective, has logistical requirements that may hinder its application during relief operations. This study evaluates the potential of a semi-decentralised water treatment approach for humanitarian emergencies. Its performance was evaluated against the relevant water quality treatment objectives (The Sphere Project) under controlled laboratory conditions using a synthetic test water. Results revealed that whilst the recommended minimum free chlorine residual levels were not attained (possibly due to high chlorine demand of test water), all other treatment objectives were within desired values, namely: <1 colony-forming unit (cfu)/100 mL with regards to thermotolerant (faecal) coliforms and <5 NTU (nephelometric turbidity units) for treated water turbidity. Given the performance of the semi-decentralised approach tested here, it could be expected to attain all treatment objectives when tested in natural surface waters. It has the potential to bridge the gap between centralised (i.e. bulk water treatment kits) and fully decentralised (i.e. household) water supply strategies.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.587
Threshold uncertainty score0.209

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.204
Teacher spread0.193 · 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 designSimulation or modeling
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
Published2014
Admission routes1
Has abstractyes

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