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Record W4296715390 · doi:10.18280/mmep.090413

Evaluation of Al-Ishaqi Irrigation Project: A Case Study Eastern Canal of the Project

2022· article· en· W4296715390 on OpenAlexvenueno aff
Abdul Hameed Sadeq Hameed, Hayder A. Al Thamiry

Bibliographic record

VenueMathematical Modelling and Engineering Problems · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicIrrigation Practices and Water Management
Canadian institutionsnot available
FundersMinistry of Water Resources
KeywordsIrrigationEnvironmental scienceDistribution uniformityWater-use efficiencyWater contentSurface irrigationIrrigation managementWater efficiencyInflowHydrology (agriculture)Agricultural engineeringEnvironmental engineeringAgronomyGeographyGeologyEngineeringGeotechnical engineeringMeteorology

Abstract

fetched live from OpenAlex

An evaluation the performance of the irrigation system for the Al-Ishaqi irrigation project for the Eastern Canal was conducted to identify management strategies that can be used to improve the operation and performance of the irrigation system. The study area is located in Salah al-Din G.0overnorate, Iraq. The field work included determining the moisture content of the soil before and after irrigation, measuring the inflow of the field to find the depth of the applied water, field monitoring, and measuring the depth of the root zone for each irrigation process. Field measurements showed that the average efficiency of water application for the two fields (A, and B) are 59.81% and 38.6%, respectively. The results of the efficiency of water application showed that farmers use water more than the actual demand, so it was possible to increase the efficiency of field water use by controlling the quantities of water that supplied and controlling the irrigation time and water distribution within the irrigated land. The results also showed that the storage efficiency of the two fields (A, and B) ranged between (39.1% to 68.28%), and (41.80% to 79.8%), respectively. As for the efficiency of water distribution, the average distribution efficiency for fields (A) and (B) was about (99.54%) and (99.33%), respectively. The irrigation efficiency evaluation done in the Al-Ishaqi Irrigation Project revealed that farmers utilize more water than is required, resulting in a substantial amount of water being lost in the fields observed in this research owing to inefficient use. If the current scenario continues, groundwater levels would undoubtedly rise, putting the irrigation system's long-term viability in jeopardy.

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)0.002
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.239

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.097
GPT teacher head0.268
Teacher spread0.171 · 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 designSimulation or modeling
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
Published2022
Admission routes1
Has abstractyes

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