MétaCan
Menu
Back to cohort
Record W4235407695 · doi:10.5383/swes.06.01.0005

Physicochemical Assessment of Rain Water of Karachi, Pakistan

2014· article· en· W4235407695 on OpenAlexvenueno aff
Mahwish Chughtai, Sana Mustafa, Majid Mumtaz

Bibliographic record

VenueInternational Journal of Sustainable Water and Environmental Systems · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Pollution Assessment
Canadian institutionsnot available
FundersUniversity of Karachi
KeywordsEnvironmental scienceWater sourceFluorideContaminationPrecipitationSulfateTotal dissolved solidsEnvironmental chemistryHealth hazardChloridePollutionPotable waterSurface waterEnvironmental engineeringChemistryWater resource managementGeographyMeteorologyEnvironmental health

Abstract

fetched live from OpenAlex

Local Precipitation (Rain) is a good source of surface water and could be a safe source of drinking water if it is free from contaminants. Many Asian countries do not have access to safe drinking water; therefore, they have no alternative but to use water from contaminated sources that poses a health hazard. In the present study, thirty three rain water samples were collected from Karachi, Pakistan during monsoon season of year 2007. The pH, electrical conductivity (EC), total dissolved solids (TDS), dissolved oxygen (DO) and hardness were immediately monitored after sample collections and possible sources of NH4, Na, K, Mg, Ca, F- , Cl- , NO3 - , CO3 -2 and SO4 -2 concentrations in rain water of Karachi city, Sindh, Pakistan were analyzed in order to distinguish safe source of drinking water. All samples were completely free from fluoride contamination while the concentration of chloride and sulfate was in range of 15.11-125 mg/l and 10.02- 72.02 mg/l indicate their presence from air pollution. Moreover, the study showed that the rain water can be harvested to extend potable and non-potable water supplies in this city.

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)0.001
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 score0.148
Threshold uncertainty score0.456

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.006
GPT teacher head0.257
Teacher spread0.250 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Sustainable Water and Environmental SystemsSame topicWater Quality and Pollution AssessmentFrench-language works237,207