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Record W2981798151 · doi:10.33002/nr2581.6853.02034

Water Crisis in Making in Iran

2019· article· en· W2981798151 on OpenAlexaff
Shahrzad Khatibi, Hasrat Arjjumend

Bibliographic record

VenueGrassroots Journal of Natural Resources · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicWater-Energy-Food Nexus Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsAridWater resourcesWater scarcityAgricultureClimate changeCrisis managementPopulationNatural resource economicsWater resource managementPoliticsBusinessDevelopment economicsGeographyPolitical scienceEnvironmental scienceEconomicsGeology

Abstract

fetched live from OpenAlex

Iran is located in an arid and semi-arid region and is currently facing a serious water crisis. The climate change, droughts and political and economic problems are believed to have aggravated the water crisis. As a result, management solutions to resolve the water crisis are critically important. This paper discusses the current status of water resources in Iran and the root causes of water crisis, including population growth, inefficient agriculture, concurrent droughts and mismanagement of available water. If Iran wants to survive, it must prioritize water resources management in the country.

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)0.001
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 score0.024
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.003
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.011
GPT teacher head0.229
Teacher spread0.219 · 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

Citations34
Published2019
Admission routes1
Has abstractyes

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