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Determination of Ground Water Quality for Agriculture and Drinking Purpose in Sindh, Pakistan

2014· article· en· W2172296152 on OpenAlexvenueno aff
Benish Nawaz Merani, Saghir Ahmed Sheikh, Shafi Muhammad Nizamani, Aasia Akbar Panhwar, Mahvish Jabeen Channa

Bibliographic record

VenueJournal of Pharmacy and Nutrition Sciences · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Pollution Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsTurbidityAlkalinityTotal dissolved solidsChemistrySodiumEnvironmental chemistryPotassiumWater qualityGroundwaterChlorideHard waterSodium bicarbonateBicarbonateEnvironmental scienceEnvironmental engineeringGeologyEcology

Abstract

fetched live from OpenAlex

The study was conducted to assess the quality of ground water from different Talukas of district Tando Muhammad Khan for drinking and agriculture purpose. Water samples for determining the water quality were collected in one liter polyethylene bags by observing standard sample collection method. It was ensured that sample collection sites must be at least 500 feet away from each other.Physical and chemical parameters of ground and surface water samples such as pH, Electrical Conductivity (EC), Turbidity, Colour, Taste, Odour, Alkalinity as CaCO3, Bicarbonate (HCO3), Carbonate (CO3), Calcium (Ca), Magnesium (Mg), Hardness, Sodium (Na), Potassium (K), Chloride (Cl), Phosphate (PO4), Total Dissolved Solids (TDS) and Arsenic (As) were determined.The study clarified that pH and odour was within the permissible limits in majority of samples whereas, Arsenic (As),Hardness, Sodium (Na),Total Dissolved Solids (TDS), Taste, Chloride (Cl) and turbidity were beyond the permissible limits set by WHO.The groundwater status in Tando Muhammad Khan district, TDS in 50% samples, Chloride in 54.16% samples, Sulphate in 44.8% samples, Calcium in 38.5% samples, Sodium in 54.16% samples, hardness in 21.88% samples were beyond the WHO’s permissible limits for human consumption.

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)0.002
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.256
Threshold uncertainty score0.124

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.052
GPT teacher head0.383
Teacher spread0.331 · 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 designBench or experimental
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
Published2014
Admission routes1
Has abstractyes

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