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Record W4292342437 · doi:10.1111/wej.12822

Failure mode effects and criticality analysis of water supply systems' risks: Path to water resources planning and policy

2022· article· en· W4292342437 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

VenueWater and Environment Journal · 2022
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsUnited Nations University Institute for Water, Environment, and Health
Fundersnot available
KeywordsFailure mode, effects, and criticality analysisWater supplyFailure mode and effects analysisRisk analysis (engineering)Risk assessmentRisk managementEnvironmental scienceWater resource managementEnvironmental engineeringEngineeringReliability engineeringBusinessComputer science

Abstract

fetched live from OpenAlex

Abstract The risk assessment of drinking‐water supply systems in Ogun State, Nigeria, was carried out using the failure mode effects and criticality analysis (FMECA) approach. The FMECA is a systemic process that identifies potentials failure modes within a system and was chosen for its causes and effect approach to assessing risks. The objective of the study was to assess drinking‐water supply systems and identify water supply systems' risks from source to point‐of‐use. Three major water supply sources were selected for assessment: hand‐dug wells, boreholes and public water supply sources. The sources were assessed by identifying the potential failure modes that exist within the water supply sources and the consequence of the identified risks on relevant stakeholders. The sources were divided into modules. The risk in each module was determined by multiplying failure rate (likelihood) and consequences of failure of the module. Risk reduction options include repair and maintenance measures, information dissemination on the procedures to reduce the identified risks and preventive and regulatory approaches. The resulting risks were characterized using FMECA risk matrix of each water source and classified into high, medium and low risks. Well cover and lining were the most risk‐prone modules for hand‐dug wells (high and medium risks). Broken well cover and lining serve as pathways to contaminants into the well. Casing and screen modules posed the highest risk for boreholes, recording high to medium risk. Cracked casing and broken screen provide access for contamination into boreholes. The module with the greatest risk for public water supply source was the point‐of‐abstraction/use module. Unsanitary containers and poor storage conditions is believed to be responsible for recontamination of the treated water Climate variability, environmental and anthropogenic influences were observed to be responsible for most of the identified risks. The study highlights that consumer participation is vital in ensuring the availability of safe drinking‐water, stressing consumer education as the most important channel. The study recommends the use of FMECA to ensure implementing preventive and regulatory measures by water monitoring agencies and for water resources planning and policy making.

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 score0.157
Threshold uncertainty score0.441

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 studies0.0010.000
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 head0.262
Teacher spread0.251 · 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