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Record W2129314265 · doi:10.5267/j.msl.2014.8.012

An optimization technique for cropping patterns and land consolidation: A case study for irrigation network

2014· article· en· W2129314265 on OpenAlexvenueno aff
Azim Shirdeli, Somayeh Dastvar

Bibliographic record

VenueManagement Science Letters · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRice Cultivation and Yield Improvement
Canadian institutionsnot available
Fundersnot available
KeywordsConsolidation (business)CroppingIrrigationAgricultural engineeringComputer scienceWater resource managementLand consolidationEnvironmental scienceBusinessGeographyAgricultureAgronomyEngineering

Abstract

fetched live from OpenAlex

During the past few decades, there has been growing interest in water resources management. Presently, there are many areas in middle east facing with shortage of water for agricultural activities. Therefore, there is a growing concern to have efficient usage of water through optimization techniques. This paper presents a study to maximize famers' revenue by developing a mathematical model subject to some land and water constraints. The proposed study has been applied for a case study of agricultural program in city of Abhar, Iran and the preliminary results indicate that it could increase the efficiency of agricultural program, significantly. In our survey, the optimal cultivation yields for four five-year programs have increased the income by 20.81, 44.698, 87.18 and 250.34 percent, respectively. In addition, 7.27% development land is added to agricultural lands, there is a 17% increase in water utilization and the productivity is increased from 50% to well above 77% after four 5-year programs have been implemented.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.389
Threshold uncertainty score0.438

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.022
GPT teacher head0.258
Teacher spread0.236 · 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

Citations5
Published2014
Admission routes1
Has abstractyes

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