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Record W2509216505 · doi:10.1021/acs.est.6b01066

Setting the System Boundaries of “Energy for Water” for Integrated Modeling

2016· article· en· W2509216505 on OpenAlexaff
Page Kyle, Nils Johnson, Evan Davies, David L. Bijl, Ioanna Mouratiadou, Michela Bevione, Laurent Drouet, Shinichiro Fujimori, Yaling Liu, Mohamad Hejazi

Bibliographic record

VenueEnvironmental Science & Technology · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicWater-Energy-Food Nexus Studies
Canadian institutionsUniversity of Alberta
FundersBiological and Environmental Research
KeywordsEnergy systemEnergy (signal processing)Environmental scienceSystems engineeringComputer scienceEngineeringRenewable energyPhysicsElectrical engineering

Abstract

fetched live from OpenAlex

Many studies over the last two decades have addressed the “water-energy nexus,” generally defined as the inter-dependency between water and energy in their supply, processing, distribution, and use. The research community currently disaggregates the water-energy nexus into two components: “water for energy” and “energy for water.” While there seems to be clear consensus on the definition of “water for energy”—that is, water required for the extraction, processing, and transformation of energy as well as the irrigation of bioenergy—there has been less agreement on the definition and system boundaries of “energy for water.” Here we represent six integrated assessment modeling teams presently incorporating the hydrologic system and water demands into existing global models of energy, agriculture, land use, and climate. In this article, we propose system boundaries of “energy for water” that are appropriate for integrated energy and water modeling, and introduce a third category of processes that are relevant for the water-energy nexus, but that are not logically classified as either “water for energy” or “energy for water.”

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
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 score0.217
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.006
Scholarly communication0.0000.000
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 head0.007
GPT teacher head0.195
Teacher spread0.187 · 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; both teacher heads agree on what is shown here.

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

Citations15
Published2016
Admission routes1
Has abstractyes

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