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The Nexus of Energy and Water Quality

2017· book· en· W2734303605 on OpenAlexaff
Erika Weinthal, Avner Vengosh, Kate J. Neville

Bibliographic record

VenueOxford University Press eBooks · 2017
Typebook
Languageen
FieldEnvironmental Science
TopicWater-Energy-Food Nexus Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNexus (standard)Hydraulic fracturingUnconventional oilNatural resource economicsWork (physics)Energy securityQuality (philosophy)Water scarcityFossil fuelCoalOil shaleWater qualityEnvironmental planningTight oilScarcityProduction (economics)Water-energy nexusBusinessPetroleum engineeringWater resourcesEnvironmental scienceEngineeringEconomicsWaste managementRenewable energy

Abstract

fetched live from OpenAlex

While the literature on the water-energy nexus tends to focus on scarcity and security, scientific research is revealing increasing concerns with the impact of energy production on water quality. This chapter explores the politics of energy and water quality, with a focus on water contamination associated with coal and shale gas development. It presents evidence of the effects of fossil fuel exploration and production on water quality, noting the life cycle water quality impacts of the coal industry and emerging work on the effects of unconventional shale gas and tight oil associated with hydraulic fracturing. While the science is drawn primarily from the United States, the chapter then considers the global implications of these findings for policy design. It argues that current regulatory approaches are mismatched with the environmental risks and calls instead for a holistic approach to policy design and management that brings together the energy and water sectors.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.010
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.013
Scholarly communication0.0060.006
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0100.001

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 head0.023
GPT teacher head0.204
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations12
Published2017
Admission routes1
Has abstractyes

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