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Record W2611903084

Use of Water Quality Indices for Water Quality Assessment in Small Island State: A Case Study in the Northern Aquifer of Mauritius

2016· article· en· W2611903084 on OpenAlexaboutno aff
Varsah Gungoa

Bibliographic record

VenueInternational Journal of Sciences: Basic and Applied Research · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Pollution Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsWater qualityAquiferEnvironmental scienceIndex (typography)Sampling (signal processing)Quality (philosophy)Hydrology (agriculture)GroundwaterWater resource managementGeologyEngineeringComputer scienceEcologyGeotechnical engineering
DOInot available

Abstract

fetched live from OpenAlex

The Water Quality Index (WQI) is a single number that expresses water quality rating by aggregating the measurement of water quality parameters. The aim of this paper is to make an assessment of the Water Quality of the groundwater in the Northern Aquifer in Mauritius by making use of water quality indices using the Brown and his colleagues and the Canadian Council of Ministers of the Environment Water Quality Index (CCME WQI) methods. Six water quality parameters were used to determine the WQI at 16 different sampling stations within the northern aquifer. The two methods provided two different ratings;

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)0.015
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 score0.030
Threshold uncertainty score0.772

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.263
GPT teacher head0.453
Teacher spread0.190 · 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

Citations4
Published2016
Admission routes1
Has abstractyes

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