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Record W4416318899 · doi:10.21608/zjar.2025.465672

PERFORMANCE EVALUATION OF SPRINKLERS (MINI AND MICRO) UNDER USING TREATED WASTE WATER FOR LANDSCAPE

2025· article· en· W4416318899 on OpenAlexaboutno aff
Basem M. Atman, Mohamed Abdel‐Wahab, Y. S. Awdallah

Bibliographic record

VenueZagazig Journal of Agricultural Research · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicIrrigation Practices and Water Management
Canadian institutionsnot available
Fundersnot available
KeywordsDistribution uniformityIrrigationSoil gradationQuarter (Canadian coin)Water pressure

Abstract

fetched live from OpenAlex

The purpose of this study was to assess the performance of two sprinkler types,mini and micro, type order to identify the best operating circumstances for achieving high application efficiency. The application efficiency of low quarter (AELQ), distribution uniformity (DU), and coefficient of uniformity (CU) were assessed at varying operating pressure (100, 150, and 200 kPa) and riser height (30, 50, and 80 cm). In order to prevent water loss and reduce the cost of the irrigation system, it was determined that the operating conditions that produced the highest coefficient of uniformity, distribution uniformity, and application efficiency of the low quarter were an operating pressure of 150 kPa and riser heights of 80 cm for mini sprinklers and 50 cm for micro sprinklers. When using treated waste water in a landscape irrigation system, the distribution uniformity values for micro sprinklers are higher than those for mini sprinklers under the same operating pressure and riser height conditions.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.126
GPT teacher head0.361
Teacher spread0.235 · 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
Published2025
Admission routes1
Has abstractyes

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