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Record W2991438501 · doi:10.21608/jesr.2017.57435

MAPPING OF QUALITY INDEX AND POLLUTION RISK ALONG ROSETTA BRANCH USING GIS TECHNIQUES

2017· article· en· W2991438501 on OpenAlexaboutno aff
H. A. A. El-Gawad, Mohamed Azzazy, Mamdouh El-Hattab, Thoraya Hasseb

Bibliographic record

VenueJournal of Environmental Studies and Researches · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Pollution Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsWater qualityEnvironmental scienceOutfallPollutionHydrology (agriculture)Index (typography)Water resource managementPollutantSurface waterGeographic information systemEnvironmental engineeringGeographyGeologyCartography

Abstract

fetched live from OpenAlex

Water quality of Rosetta Branch may be changed by several factors in the last decades as a result of anthropogenic activities. So, it’s important to study water class along Rosetta Branch. This research focused on assessment surface water quality along Rosetta Branch using Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI) supported by Egyptian guidelines of law 48/1982 decision 92/2013. Rosetta Branch received most pollutants from two hot spots namely: El-Rahawy drain and industrial activities in Kafr El-Zayat city. The water quality of eight sites-the main surface water resource for Delta Barrage, Edfina Barrage, Rahway, Sable, Tharir, Zawieyt El-bahr, Tala drains outfall and outlet of Rosetta Branch on Mediterranean Sea were investigated. The Geographic Information System (GIS) was used for mapping the WQI variations in different sites. The results of the index average for different sites location at each site showed that the water quality in the studied sites ranked as marginal or fair. This is attributed to the fact of the concentrations of heavy metals, ammonia and organic matter increase in sites that are more affected by drainage water from different drains in the study area. Great efforts are needed and wastewater must be treated before draining it into the River Nile water

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.134
Threshold uncertainty score0.546

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.124
GPT teacher head0.393
Teacher spread0.269 · 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
Published2017
Admission routes1
Has abstractyes

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