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Record W2134104703 · doi:10.4314/jfas.v5i2.8

Assessment of water quality index for groundwater of Valsad district of south Gujarat (India)

2015· article· en· W2134104703 on OpenAlexaboutno aff
Purvesh Shroff, RT Vashi

Bibliographic record

VenueJournal of Fundamental and Applied Sciences · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Pollution Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsAlkalinityGroundwaterTotal dissolved solidsWater qualityFluorideEnvironmental scienceEnvironmental engineeringEnvironmental chemistryHydrology (agriculture)ChemistryGeology

Abstract

fetched live from OpenAlex

The present study aims the assessment of the water quality index (WQI) for the groundwater of Valsad district of South Gujarat. Total fifteen sampling stations from five talukas of Valsad district were selected and groundwater samples were collected for two years (from August 2007 to July 2009). In this present study, WQI created by Canadian Council of Minister of the Environment (CCME) was used. For calculating the WQI, groundwater samples were analyzed for seventeen physico-chemical parameters like pH, Colour, Electrical Conductivity (EC), Total Hardness (TH), Calcium (Ca), Magnesium (Mg), Total Alkalinity (TA), Total Dissolved Solids (TDS), Silica, Chloride, Sulphate, Fluoride, Sodium, Chemical Oxygen Demand (COD) and metals like Copper (Cu), Lead (Pb) and Manganese (Mn). The WQI for Valsad district suggests that the groundwater quality is marginal. Keywords: Ground Water, Water quality index, Valsad District, Gujarat

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 score0.021
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.080
GPT teacher head0.344
Teacher spread0.263 · 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

Citations3
Published2015
Admission routes1
Has abstractyes

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