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State of Water Supply Sources and Sanitation in Nigeria: Implications for Muslims in Ikare-Akoko Township

2009· article· en· W2612864086 on OpenAlexvenueno aff
A.O. Ayeni, Alabi Soneye, I. Balogun

Bibliographic record

VenueArab world geographer · 2009
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsnot available
Fundersnot available
KeywordsSanitationWater supplyHygieneMalnutritionSocioeconomicsPopulationEnvironmental healthGeographyOpen defecationBusinessWater resource managementEnvironmental protectionEconomic growthMedicineEnvironmental engineeringEnvironmental scienceEconomics

Abstract

fetched live from OpenAlex

Access to sanitation and water supply is a fundamental need and a human right, vital to the life, health, and dignity of human beings. According to the World Health Organization, improved water supply and adequate sanitation would result in a 25 % to 33 % reduction in diarrheal diseases in the developing world, which now accounts for 4 billion cases each year; decreased incidence of intestinal worm infestations that lead to malnutrition, anemia, and retarded growth; and control of blindness due to trachoma and schistosomiasis, which are also water related. In Nigeria, less than 50 % of the population have access to improved water supply and sanitation. The percentage varies from urban to rural communities and from cities to villages. Ikare-Akoko is one of the towns that suffers from deficient water supply and sanitation. This study was carried out to establish the implications of unsafe water-supply sources and poor sanitation on Muslims in Ikare-Akoko Township, Nigeria. The study revealed that the main s...

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.268
Teacher spread0.254 · 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 designObservational
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

Citations8
Published2009
Admission routes1
Has abstractyes

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