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Record W2159690028 · doi:10.5539/sar.v4n1p1

Water Policy Under Risk and Uncertainty A Dynamic Evaluation Model of Fodder Cultivation in Oman

2014· article· en· W2159690028 on OpenAlexvenueno aff
Kheiry Hassan M. Ishag, Hag Hamad Abdelaziz

Bibliographic record

VenueSustainable Agriculture Research · 2014
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsnot available
Fundersnot available
KeywordsSustainabilityAgricultureAttractivenessInvestment (military)IrrigationGovernment (linguistics)BusinessProduction (economics)Environmental economicsWater resourcesNatural resource economicsEnvironmental scienceAgricultural engineeringEconomicsGeographyEngineering

Abstract

fetched live from OpenAlex

The continuous cultivation of the Rhodes Grass in Batinah costal area and Salalah region of Sultanate of Oman has a negative impact on the overall agriculture system and production. Improvement of the conditions could be achieved by introducing new water policy into farming and using Government suppor­ting tools to motivate farmers and achieve financial sustainability. The new water policy and strategies formed by Government are examined in three cultivated locations in this paper: Salalah location with enough irrigation water, Hanfeet location with low irrigation water and Dawkah location with very low irrigation water. Economic efficiency of the location is evaluated through the Net Present Value and IRR calculation. Within the assumption of the objective evaluation of input parameters, we can expect an acceptable economic efficiency of the investment only in Salalah location. The simplified deterministic evaluation of economic efficiency is formed to identify the relevant risk factors, followed by its quantification by the simulation processes. Taking the risk into account leads to a significant decrease of the economic attractiveness of stakeholders and more Government support is needed to achieve water policy and project sustainability at new location at Hanfeet and Dawkah location.

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 score0.017
Threshold uncertainty score0.285

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.015
GPT teacher head0.280
Teacher spread0.265 · 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
Published2014
Admission routes1
Has abstractyes

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