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Risk Zone Mapping of Lead Pollution in Urban Groundwater

2012· article· en· W2320436598 on OpenAlexvenueno aff
Azhar Siddique, Nayyer Alam Zaigham, Shiekh Mohiuddin, Majid Mumtaz, Sumayya Saied, Khalil A. Mallick

Bibliographic record

VenueJournal of Basic & Applied Sciences · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsnot available
Fundersnot available
KeywordsGroundwaterKrigingPollutionEnvironmental scienceGeographic information systemHydrology (agriculture)Water resource managementContaminationGroundwater pollutionGeographyGeologyCartographyAquifer

Abstract

fetched live from OpenAlex

Groundwater samples (n = 230) were collected from various parts of the Karachi City (Pakistan), Karachi is an urban coastal City situated at the southern most part of the Pakistan along the Arabian Sea. The groundwater samples were subjected to electrothermal atomic absorption spectroscopy (EAAS) for the analysis of Pb. Variable Pb levels were observed in groundwater samples from different parts of the city. The relative higher concentrations of Pb were found in the industrial area of the Lyari River vicinity and along the coastal belt. GIS risk zone model based on disjunctive kriging were generated and areas associated with higher risk for Pb contamination were classified on the map. The outcomes of the study stressed that GIS spatial analysis could be a useful tool for the assessment and forecasting of health risk in complex urban environmental setup.

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)0.004
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 score0.249
Threshold uncertainty score0.477

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.021
GPT teacher head0.237
Teacher spread0.216 · 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
Published2012
Admission routes1
Has abstractyes

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