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Record W4297963561 · doi:10.1016/j.ecolind.2022.109501

Costs and benefits of the development methods of drinking water quality index: A systematic review

2022· review· en· W4297963561 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueEcological Indicators · 2022
Typereview
Languageen
FieldEnvironmental Science
TopicWater Quality and Pollution Assessment
Canadian institutionsnot available
FundersHenan University of Chinese Medicine
KeywordsComputer scienceFuzzy logicRobustness (evolution)Data miningWater qualityEntropy (arrow of time)Analytic hierarchy processIndex (typography)Quality (philosophy)Operations researchArtificial intelligenceMathematics

Abstract

fetched live from OpenAlex

The drinking water quality index (DWQI) transforms multiple water quality parameters into a dimensionless number, thus, presenting comprehensive status of drinking water in an intuitive manner. However, there are very few studies summarize the current progress of DWQI. Thus, we systematically reviewed 514 articles to evaluated the methods used in each DWQI developmental step with the aim of helping environmental workers choose the most appropriate index-generation model for local application. We observed that existing studies usually select 10–15 (55.4% of the studies) physicochemical parameters (such as Cl, pH, SO4, Ca, Mg etc.) to develop a DWQI. The weights of selected parameters are most often assigned using the five-scale method (53.7%), but these values varied considerably among the different studies due to the lack of clear evaluation standards. Semiquantitative and quantitative methods have been applied to overcome the subjectivity involved in these steps, including the analytical hierarchy process, information entropy, and factor analysis etc. The measurement results are normalized using the permissible limit, and multiplied by the corresponding weight, then added up to get the final DWQI result. Specifically, two distinct approaches, fuzzy logic and WQI adopted by Canadian Council of Ministers of the Environment (CCME-WQI) are discussed. Comparing with the more common approach based on classic set theory, fuzzy logic can better resolve the inherent uncertainty in the assessment of DWQI, whereas, the CCME-WQI is more appropriate for evaluating the spatiotemporal variations in DWQI over a given period. Some studies have assessed the robustness of the developed DWQI by conducting sensitivity analyses and its effectiveness was validated by comparisons with expert scores, existing WQIs or toxicological endpoints. Fifty-seven predefined classification schemes have been proposed to interpret DWQI value. As no one-size-fits-all approach exists for DWQI development, we recommended here to clarify the principles to be followed at each step, disclose the details of each method, and validate the developed index in future research. Meanwhile, additional efforts are required to develop new water quality monitoring methods and conduct DWQI studies on central water supply system.

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)0.007
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.831
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.124
GPT teacher head0.401
Teacher spread0.277 · 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