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Record W4389538334 · doi:10.52293/wes.3.3.5964

Impact of sub-rivers feeding the Shatt Al- Arab River on its water quality

2023· article· en· W4389538334 on OpenAlexaboutno aff
Zuhal Abdulhadi Hamza

Bibliographic record

VenueWater and Environmental Sustainability · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Pollution Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsWater qualityEnvironmental scienceHydrology (agriculture)GeologyEcologyBiology

Abstract

fetched live from OpenAlex

This study's objective was to evaluate the physicochemical characteristics of the Shatt Al-Arab River in the Basra Governorate for irrigation purposes.These characteristics were pH, EC, Total Dissolved Solids, Calcium, Magnesium, Total Suspended Solids, and Nitrate.The Canadian Council of Ministers of the Environment (CCME) Water Quality Index (WQI) was applied to the analytical data of the parameters in order to fulfill the goal and produce a single value that was used to rank the river at each of the sample stations.The findings showed that some parameters studied in the Shatt Al-Arab River increased as it flowed through the study area, which could be primarily attributed to rising wastewater discharges into the river, which has a negative impact on the WQI values for these stations, which range from 41.6 to 43.6.As a result of these analyses, the Shatt Al-Arab River's water quality at the station's Karma Ali and Al-Sindbad is classified as "Poor quality" for irrigation purposes.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.121
Threshold uncertainty score0.999

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.024
GPT teacher head0.297
Teacher spread0.272 · 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.

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
Published2023
Admission routes1
Has abstractyes

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