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Record W3196444403 · doi:10.33899/rjs.2021.168912

Assessment of the Water Qualitative Characteristics of the Tigris River Passing Through the City of Mosul and Calculating the Water Quality Index Coefficient

2021· article· en· W3196444403 on OpenAlexaboutno aff
Hiba F. A. Shihab, Abdalrahman Kannah

Bibliographic record

VenueMağallaẗ ʻulūm al-rāfidayn · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Pollution Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsAlkalinityTurbidityWater qualityTotal dissolved solidsHydrology (agriculture)Environmental sciencePotassiumSalinityEnvironmental engineeringEnvironmental chemistryChemistryGeologyEcologyGeotechnical engineeringBiology

Abstract

fetched live from OpenAlex

The quality of the water of the TigrisRiver passing through the city of Mosul was studied, starting from the Kubba area in the north to the Yarmajah area in the south, and the TigrisRiver is the only water resource for the city. The Physical and Chemical propertiesof river water was conducted, which included (water temperature, pH, electrical conductivity, total dissolved solids, turbidity, dissolved oxygen and some positive and negative ions), and the Canadian Water Quality Index = (WQI) was used to express the quality of river water. Tigris as it passes in the city of Mosul. The results indicated that the water of the TigrisRiver passing through the city of Mosul tends towards alkalinity, and the river water has good ventilation, as the average dissolved oxygen values ​​ranged between (8.3-9.3) mg / liter. The ions of nitrates, orthophosphates, sulphates and chlorides showed a variation in their concentrations during the study period. The rates of sodium and potassium ion concentrations ranged between (19.9-24.6) and (2.6-4.2) mg / liter, respectively. The studied water was classified as (good - moderate) for drinking, as the values ​​of the water quality index ranged between (76.7-91.2). The index values ​​ranged between (86.3-99.6) and the water was classified as excellent too good for the river conservation system.

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)0.003
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 score0.425
Threshold uncertainty score0.639

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.040
GPT teacher head0.341
Teacher spread0.301 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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