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Record W3042502559 · doi:10.1038/s41467-020-17400-4

Future changes in the trading of virtual water

2020· article· en· W3042502559 on OpenAlexaff
Neal T. Graham, Mohamad Hejazi, Son H. Kim, Evan Davies, Jae Edmonds, Fernando Miralles‐Wilhelm

Bibliographic record

VenueNature Communications · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicWater-Energy-Food Nexus Studies
Canadian institutionsUniversity of Alberta
FundersPacific Northwest National LaboratoryBattelleU.S. Department of EnergyOffice of ScienceNational Oceanic and Atmospheric AdministrationNational Science Foundation
KeywordsVirtual waterNatural resource economicsAgricultureBusinessEnvironmental scienceNon-renewable resourceGroundwaterRenewable energyWater resource managementEconomicsWater scarcityGeographyEcologyGeology

Abstract

fetched live from OpenAlex

Abstract Water stressed regions rely heavily on the import of water-intensive goods to offset insufficient food production driven by socioeconomic and environmental factors. The water embedded in these traded commodities, virtual water, has received increasing interest in the scientific community. However, comprehensive future projections of virtual water trading remain absent. Here we show, for the first time, changes over the 21st century in the amount of various water types required to meet international agricultural demands. Accounting for evolution in socioeconomic and climatic conditions, we estimate future interregional virtual water trading and find trading of renewable water sources may triple by 2100 while nonrenewable groundwater trading may at least double. Basins in North America, and the La Plata and Nile Rivers are found to contribute extensively to virtual water exports, while much of Africa, India, and the Middle East relies heavily on virtual water imports by the end of the century.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0000.000
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 head0.028
GPT teacher head0.251
Teacher spread0.223 · 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 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

Citations144
Published2020
Admission routes1
Has abstractyes

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