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Record W4310534773 · doi:10.30574/ijsra.2022.7.2.0277

A study of physico-chemical parameters of well and bore hole water in Pozhikkarai to Manavalakkurichi South Coast of Tamil Nādu

2022· article· en· W4310534773 on OpenAlexaff
MV Reena, A Amalraj, R Ajitha, C. Nirmala Louis

Bibliographic record

VenueInternational Journal of Science and Research Archive · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Pollution Assessment
Canadian institutionsSt. Jerome's University
Fundersnot available
KeywordsAlkalinityTurbidityTotal dissolved solidsSalinityWater qualityNitrateEnvironmental scienceFluoridePotassiumEnvironmental chemistryGroundwaterChlorideHydrology (agriculture)ChemistryEnvironmental engineeringGeologyEcologyInorganic chemistryOceanography

Abstract

fetched live from OpenAlex

A systematic analysis has been carried out to explore physico-chemical parameters of ground water from well and bore hole in four different stations (sites) Pozhikarai, Periyakadu, Muttom, and Manavalakurichi in Kanyakumari District of South India. Well and bore hole water samples were collected from four stations in different seasons & in two years and analyzed for temperature, pH, turbidity, alkalinity, hardness, salinity, fluoride, chloride, total dissolved solids, dissolved oxygen, BOD, electrical conductivity, total nitrogen, nitrate, sulphate, ammonia, phosphate, total phosphorus, sodium, potassium and oxidation & reduction potential. Comparative studies of parameters in different stations and in different seasons in different years were also carried out. The physico-chemical parameters were analyzed and the results were compared with water quality standards described by WHO. The above study is useful to know the water quality and their fitness for drinking purposes at various stations undertaken.

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 score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
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 head0.044
GPT teacher head0.359
Teacher spread0.314 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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