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Record W7071866471

Using The Canadian Method to Classify Irrigation Water Quality Index for Shatt Al- Kufa River Section (Al–Zerkh to Al–Qadisiya)

2017· article· en· W7071866471 on OpenAlexaboutno aff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Pollution Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsSodium adsorption ratioIrrigationWater qualityHydrology (agriculture)Surface waterIndex (typography)Sulfate
DOInot available

Abstract

fetched live from OpenAlex

Shatt Al-Kufa ( Kufa River) is the main supply of surface waters in Najaf, it is necessary to concern about the river water quality because of increasing demand for different using. This study aims to find the Water Quality Index ( WQI) for irrigation use at many locations on Shatt Al-Kufa within the study section of the river. Nine water quality parameters: Sulfate (SO4 -2), Chlorides ( Cl-1), Total Dissolved Solid (TDS), Electrical Conductivity (EC), Hydrogen Power ( pH), Calcium (Ca+2 ), Magnesium ( Mg+2 ), Sodium (Na+ ), and Potassium ( K+) were studied over ten months ( January- October 2014) for nine locations along the study section. Sodium Adsorption Ratio ( SAR) and Soluble Sodium Percentage ( SSP) were also calculated. Firstly Food Agricultural Organization ( FAO, 1985) was adopted to evaluate the suitability of water for irrigation use. Then Canadian Water Quality Index ( CWQI ) was applied to classify the water quality index of the river for irrigation. It is found that the mean values of parameters for all selected locations are within the highest allowable limits of FAO classification for irrigation, with respect to the values of ( SAR ) the results showed that the water samples were within the level ( S1 ) which there was no harmful effects from sodium, while ( SSP ) values ( except one value ) fall within the water class of good according to Todd classification of irrigation water based on SSP. Results of the annual and seasonal average water quality indices according to the Canadian method were classified as Fair ( 65 – 79 ) in all selected locations. The effective reason which decrease the water quality index, was the presence of high values of ( EC ) and high concentrations of ( TDS ) in all locations, which it refers to high concentrations of salts. Approximately 100 % of EC and TDS water samples have concentrations that exceeded the permissible limits for irrigation 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.521
Threshold uncertainty score0.964

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.006
Science and technology studies0.0010.000
Scholarly communication0.0010.000
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 head0.504
GPT teacher head0.624
Teacher spread0.120 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2017
Admission routes1
Has abstractyes

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