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Record W3040336774 · doi:10.22059/ees.2020.43229

The food-energy-water nexus: A framework for sustainable development modeling

2020· article· en· W3040336774 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEnvironmental Engineering Science · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicWater-Energy-Food Nexus Studies
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsNexus (standard)Food energyWater energyPopulationEnvironmental economicsWater resourcesFood systemsPopulation growthNatural resource economicsBusinessEnvironmental resource managementEnvironmental scienceFood securityEngineeringEconomicsAgricultureEcology

Abstract

fetched live from OpenAlex

Energy, water, and food are facing present and future challenges triggered by climate change, population growth, human behavior, and economics. Management strategies for energy, water, and food are possible through policies, technology, and related education. However, the links between resources (energy, water, and food) and impacting factors (population increase, human behavior, economics, and global warming) need to be developed. Holistic modeling is needed to supply and demand energy, water, and food. That type of modeling explores the energy-water-food nexus. The framework for such modeling is described in this study, and previous frameworks are reviewed. Recommendations for addressing energy, water, and food challenges, before and after completing the energy-water-food nexus modeling, involve the following: modifying processes, modifying products, innovative processes, and innovative products. With an energy water-food-nexus model, the impact of any changes on resources can be measured and quantified.

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.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.856
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 studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.010
GPT teacher head0.176
Teacher spread0.166 · 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