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Record W2342333598 · doi:10.1080/02626667.2016.1164316

Demand modulation of water scarcity sensitivities to secular climatic variation: theoretical insights from a computational maquette

2016· article· en· W2342333598 on OpenAlexaff
Sean W. Fleming

Bibliographic record

VenueHydrological Sciences Journal · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsScarcityNexus (standard)EconomicsWater scarcityVariance (accounting)Climate changePerspective (graphical)Supply and demandSecular variationPopulationEconometricsNatural resource economicsWater resourcesMicroeconomicsEcologyMathematicsComputer scienceGeology

Abstract

fetched live from OpenAlex

Increased demand associated with population or economic growth, and decreased supply under some climatic shifts, obviously contribute to water scarcity. As a fresh perspective, we offer a generic theoretical treatment using a computational “maquette”, employing parameterizations to avoid assumptions about the origin and scale of climate and demand changes. The results suggest a distinct (and more subtle) point: the sensitivities of water stress to changes in both the mean and the variance of hydroclimate are modulated by demand level. Theoretical behaviours generated by the reduced-complexity model are surprisingly intricate, including profound nonlinearities and bifurcations. These may form a lower bound on the dynamical complexity of the demand–supply–scarcity nexus. Overall, the outcomes suggest that demand growth substantially intensifies and nonlinearizes water stress sensitivities to secular climate variation, and, in particular, that the interactions between demand changes and second-order hydroclimatic non-stationarity may produce non-intuitive water scarcity impacts requiring much closer study.EDITOR A. Castellarin; ASSOCIATE EDITOR N. Ilich

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 categoriesInsufficient payload (model declined to judge)
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 score0.628
Threshold uncertainty score0.997

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.017
GPT teacher head0.231
Teacher spread0.214 · 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.

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

Citations2
Published2016
Admission routes1
Has abstractyes

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