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Record W3104090027 · doi:10.26480/bdwre.01.2020.22.31

AN OVERVIEW ON EMERGING WATER SCARCITY CHALLANGE IN PAKISTAN, ITS CONSUMPTION, CAUSES, IMPACTS AND REMEDIAL MEASURES

2020· article· en· W3104090027 on OpenAlexaff
Saddam Hussain, Saba Malik, Muhammad Jehanzeb Masud Cheema, Muhammad Umair Ashraf, Muhammad Sohail Waqas, Muhammad Mazhar Iqbal, Sikandar Ali, Lubna Anjum, Muhammad Aslam, Hassan Afzal

Bibliographic record

VenueBig Data In Water Resources Engineering (BDWRE) · 2020
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsUniversity of Prince Edward Island
Fundersnot available
KeywordsWater scarcityNatural resource economicsScarcityWater resourcesFood securityWater securityBusinessPopulationAgricultureIndustrialisationUrbanizationPopulation growthNatural resourceWater resource managementEnvironmental planningGeographyEconomicsEconomic growthEnvironmental scienceEcology

Abstract

fetched live from OpenAlex

Around two-thirds of the earth’s surface covered with water, it is obvious that water is among the most significant aspects that are essential for the life of human being. It is not only important to live, but also important to take a part in social and financial development. Water is God’s personalized gift, as well as the necessity of life. Due to high level of gluttony, misused play of people with the environment and the global climate, shift results in progressive diminishing of natural resources. The problems of freshwater and food security at global level linked to the overwhelming population of the world. To overcome the food and water scarcity challenge, there is required to transfer the freshwater from agriculture sector to other straining purposes. Pakistan is facing the meager water scarcity crises in the last few years, as water is becoming scarce to any specified usage. The quickly growing population, expansion of drainage regions, increasing urbanization and industrialization put a lot of stress on available water supplies. At the moment, there is dire need to harvest rain water by constructing more dams and focus on effective management strategies for further use. Specifically, authors are recommended that, water should be provided demand base instead of supply base irrigation system. Moreover, in addition to land tax, tax on irrigation water quantity/usage should be rectified.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.155
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.000
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 head0.105
GPT teacher head0.278
Teacher spread0.173 · 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.

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

Citations29
Published2020
Admission routes1
Has abstractyes

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