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Water Security, The Nexus Of Water, Food, Population Growth and Energy

2016· article· en· W2537917590 on OpenAlexaff
Edward A. McBean

Bibliographic record

VenueThe Global Environmental Engineers · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicWater-Energy-Food Nexus Studies
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsFood securityWater securityNexus (standard)Population growthPopulationUrbanizationClimate changeFood energyGeographyWater supplyEnvironmental scienceWater resource managementEnvironmental protectionWater resourcesNatural resource economicsAgricultureEcologyEnvironmental healthBiologyEnvironmental engineeringEconomicsEngineering

Abstract

fetched live from OpenAlex

Issues causing increased water stress and availability throughout the world are complex. The importance of supply-side issues arising from increasing urbanization, causing localized levels of water stress, is described. Further, while the world population has doubled over the last 50 years, water use has tripled. Water use rates have increased (from 400km3 per year per billion people in 1965, to the current level of 600km3 per year per billion people in 2015), as a result of population growth with its associated food and energy implications, and dietary shifts of populations.Water demands in 2025 are projected to be 1500km3 per year, or 60% more than volumes in 2015. The findings in a case study in the Zambezi River basin indicate that while climate change is projected as 25% of the projected impact to future water security issues, 75% of water security issues are attributable to population increases (and its related food, energy, and changing dietary habits) and hence, population increases represent a greater threat to water security.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.190
Threshold uncertainty score0.360

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.003
GPT teacher head0.155
Teacher spread0.151 · 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 designBench or experimental
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

Citations8
Published2016
Admission routes1
Has abstractyes

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