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Record W2013003943 · doi:10.4296/cwrj30011

Using Economic Instruments for Water Demand Management: Introduction

2005· article· en· W2013003943 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.

venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueCanadian Water Resources Journal / Revue canadienne des ressources hydriques · 2005
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsnot available
Fundersnot available
KeywordsIndustrial waterBusinessWater sectorWater resourcesWater useNatural resource economicsWater qualityQuality (philosophy)Environmental economicsWater conservationWater industryEconomic sectorSecondary sector of the economyWater supplyEnvironmental planningEnvironmental scienceEconomicsEnvironmental engineeringEngineeringEconomyWaste management

Abstract

fetched live from OpenAlex

Water is an important input for many industrial sectors including manufacturing, mining and energy generation. Industrial water use differs from other sectors in its high reliance on self-supplied water, the potential for internal water recycling and the possibility of use leading to diminished water quality. Furthermore, industrial water use has a number of interrelated components including intake, internal recirculation, treatment prior to and following use and discharge. In principle, each of these activities can be expected to depend upon the economic and regulatory environment facing the firm. This paper examines the economic characteristics of Canadian industrial water use and considers the experiences of other jurisdictions in employing economic instruments to promote industrial water conservation. The paper then assesses the potential efficacy of economic instruments as a means of promoting integrated water resources management in the Canadian industrial sector. The paper concludes by identifying the opportunities and barriers for enhanced reliance on economic instruments.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.884
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
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.012
GPT teacher head0.188
Teacher spread0.176 · 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