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Record W4311376164 · doi:10.33899/rjs.2022.176072

Assessment the Quality Number of Well Water on the Left Side of the City of Mosul / Iraq and its Suitability for Drinking Using the Canadian Water Quality Index.

2022· article· en· W4311376164 on OpenAlexaboutno aff
Qusay Muhammad, Abdulmoneim M. A. Kannah

Bibliographic record

VenueMağallaẗ ʻulūm al-rāfidayn · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Pollution Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsAlkalinityTurbidityWater qualitySulfateTotal dissolved solidsEnvironmental scienceEnvironmental engineeringEnvironmental chemistryChemistryHydrology (agriculture)Geology

Abstract

fetched live from OpenAlex

The current study was conducted on 10 wells of water located within the city of Mosul / on the left side. The quality of this water was evaluated to determine the number of physical and chemical properties, including acidity, turbidity, total dissolved salts, total alkalinity, nitrates, chlorides, phosphates, calcium, magnesium, and sulfates. The current study showed that the values ​​of total dissolved solids and sulfate rates for well water ranged between 155-1150 mg/L, and 226-1037mg/L, respectively, and the acidity function rates ranged between 7-7.6, which are within The appropriate determinants of drinking according to the specifications of the World Health Organization, while the average values ​​of turbidity ranged between 0.5-21 N.T.U, and the nature of the geological formations of the study area had an impact on the concentration of calcium and magnesium, as well as the chlorides and total alkalinity where the concentration rate ranged Between 29-325, 16-89, 20-259 and 179-366 mg/L, respectively. The results of the current study showed that wells water for drinking purposes, according to the Canadian Water Quality Index (CWQI), varied between (doubtful - good).

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)0.010
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient 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 score0.029
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.000
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 science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.063
GPT teacher head0.339
Teacher spread0.276 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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