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Record W4408803359 · doi:10.26480/wcm.01.2024.79.86

THE USE OF WATER QUALITY INDICES IN EVALUATING THE TAP WATER QUALITY: THE CASE OF AL-BAQA’A REFUGEE CAMP, JORDAN

2023· article· en· W4408803359 on OpenAlexaboutno aff
Marwa A. Izmeqna, Shadi Moqbel

Bibliographic record

VenueWater Conservation and Management · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Pollution Assessment
Canadian institutionsnot available
FundersUniversity of Jordan
KeywordsTap waterRefugeeWater qualityQuality (philosophy)Water resource managementEnvironmental scienceGeographyEnvironmental engineeringPhysicsArchaeologyBiology

Abstract

fetched live from OpenAlex

Many people find tap water unsatisfactory for their daily consumption, despite the fact that suppliers are required to provide high-quality water to the public. Water quality indices that were developed to assess the water quality of water bodies are sometimes used to assess water in distribution networks. The use of the water quality indices Weighted Arithmetic (WA) and the Canadian Council of Ministers of the Environment (CCME) methods in describing the tap water quality status at Al-Baqa’a camp is evaluated in this study. Forty samples were collected over four rounds from ten households. The study concluded that the water supplied to the study area was safe but was subjected to a later irregular localized biological contamination. The analysis of water constituents revealed that both indices provide generalized descriptions that do not accurately represent the current state of tap water quality. The study proposed using a two-index system comprised of WQI at the network pumping location and a contamination threat index to study tap water quality. Applying the two indices system at Al-Baqa’a Camp tap water showed that water supplied to the area is safe but it has a localized biological contamination potential of 23.7%

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)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.436
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
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 science0.0000.001
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 head0.166
GPT teacher head0.379
Teacher spread0.213 · 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 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

Citations1
Published2023
Admission routes1
Has abstractyes

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