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Record W7117875217 · doi:10.33899/injes.v26i1.56019

Evaluation of Hydrochemistry of Kirkuk Irrigation Project, Kirkuk, Northern Iraq

2025· article· en· W7117875217 on OpenAlexaboutno aff
Hiba Sultan, Soran Nihad Sadiq

Bibliographic record

VenueIraqi National Journal of Earth Science (INJES) · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicGroundwater and Watershed Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsIrrigationHydrology (agriculture)TurbidityGroundwaterIrrigation statisticsWater qualitySalinityAgriculture

Abstract

fetched live from OpenAlex

The irrigation project is considered one of the important projects in Iraq, as the study area is one of the important agricultural areas, which mostly depends on groundwater wells as well as surface water from the Kirkuk Irrigation Project. This study aims to estimate the irrigation project for drinking, agriculture, and irrigation purposes. (46) Water samples were collected in humid and dry seasons in (2023-2024) and analyzed; several methods and classifications were used, including comparison with Canadian standards, Piper hydrochemical facies classification, and classifications for irrigation purposes. The major and minor elements and heavy metals were analyzed. The physical properties were measured, including pH, electrical conductivity, total dissolved solids, turbidity, and total hardness. It indicates that the Piper scheme of the hydrochemical facies is calcium-magnesium-bicarbonate. The results show, according to the Canadian water quality index, that the water is poor and unsuitable for human use, and does not have an excellent water condition due to turbidity and calcium concentration, pH, and magnesium values exceeding the permissible limit according to WHO and IQS, so it is considered unsafe for drinking. The results show, according to the classifications of SAR, MH, %Na, SP, salinity hazard, and Wilcox, that the study area is generally suitable and good for irrigation and agricultural uses for most soils and agricultural crops. Analyzed Pearson’s correlations in some of the studied parameters in both seasons reveals that the correlation coefficient is strong and significant positive and negative.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.203
Threshold uncertainty score0.388

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.022
GPT teacher head0.297
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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueIraqi National Journal of Earth Science (INJES)Same topicGroundwater and Watershed AnalysisFrench-language works237,207