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Record W4224998701 · doi:10.4314/ijbcs.v15i7.2s

Assessment of physical conditions and proposed best management practices of domestic storage tanks supplied by a water utility in a rapidly growing city

2022· article· en· W4224998701 on OpenAlexfundno aff
E. W. Makoko, E. Wozei

Bibliographic record

VenueInternational Journal of Biological and Chemical Sciences · 2022
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsnot available
FundersMcGill University
KeywordsWater storageStorage tankEnvironmental scienceWater qualityWater supplyWater utilityChecklistContaminationEnvironmental engineeringWater tanksWaste managementToxicologyEnvironmental healthEngineeringMedicineEcology

Abstract

fetched live from OpenAlex

The physical conditions of domestic storage tanks for customers supplied by a water utility were assessed in Kampala, a rapidly growing City in Uganda. A longitudinal assessment of 372 storage tanks in 6 sampled administrative Wards with a minimum of 6 samples collected from each site in both wet (March-May) and dry (June-August) months of 2017 was carried out. A set of guiding questions were used to establish tank conditions with a YES or NO response and a range of low to critical risk rating. It was revealed that there was a statistically significant relationship (p=0.001) between tank physical conditions and quality of stored water. Two of six Wards in the City had high levels of water contamination related to domestic water storage tanks with poor sanitary conditions and contaminated water with E. coli. The study therefore revealed that the physical conditions and management of domestic water storage tanks have an effect on water quality. This is important information for a water utility as it means that it is not enough to supply safe water if the quality may deteriorate upon storage at the consumer premises. A routine inspection checklist and consumer guidelines for domestic storage tank management are proposed.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.286
Threshold uncertainty score0.151

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 head0.032
GPT teacher head0.357
Teacher spread0.325 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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