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Record W2891706207 · doi:10.3390/w10091173

Explaining Water Pricing through a Water Security Lens

2018· article· en· W2891706207 on OpenAlex
Tariq A. Deen, Nidhi Nagabhatla, Gustavo Ayala

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

VenueWater · 2018
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsMcMaster UniversityUnited Nations University Institute for Water, Environment, and Health
FundersMinistry of EnvironmentMinisterio del Ambiente, Agua y Transición Ecológica
KeywordsWater pricingWater securityWater resourcesWater scarcityInternational trade and waterWater conservationIntegrated water resources managementFood securityWater industryEnvironmental economicsBusinessWater useWater supplyNatural resource economicsWater resource managementEnvironmental scienceEconomicsEnvironmental engineeringAgriculture

Abstract

fetched live from OpenAlex

Can water security serve as a platform for developing a long-term solution to ongoing water crises? Many regions around the world are experiencing severe water problems, including water scarcity, water-borne diseases, water-related natural hazards, and water conflicts. These issues are expected to increase and intensify in the future. Both developed and developing economies face a water supply and demand imbalance that will potentially influence their water pricing structures. Institutions and policies that govern the pricing of this natural capital remain crucial for driving food production and providing services. The complex and multifaceted issues of sustainable water management call for a standard set of tools that can capture and create desired water security scenarios. Water pricing is an important contributing factor for achieving these scenarios. In this paper, we analyze how water pricing can be used as a tool to enact the water security agenda. This paper addresses these issues from three facets: (1) Economic aspects—the multiple processes through which water is conceptualized and priced; (2) analysis of water pricing considering its effect in water consumption; and (3) arguments for assessing the potential of water pricing as a tool to appraise 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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.527
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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.012
GPT teacher head0.195
Teacher spread0.182 · 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