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Record W3167077459 · doi:10.1080/15567036.2021.1931569

Toward the deployment of nuclear cogeneration projects – issues and considerations

2021· article· en· W3167077459 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEnergy Sources Part A Recovery Utilization and Environmental Effects · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicRisk Perception and Management
Canadian institutionsUniversity of Ontario Institute of Technology
Fundersnot available
KeywordsCogenerationSoftware deploymentNuclear powerEnvironmental economicsBusinessElectricityDiversification (marketing strategy)Renewable energyElectricity generationEngineeringPower (physics)EconomicsMarketing

Abstract

fetched live from OpenAlex

Nuclear cogeneration can lead to higher overall energy efficiency and enhanced utilization of a nuclear power plant. It enables diversification of the role they play in the energy market (i.e., heat and transportation along with power generation) and contributes to its decarbonization. The deployment of nuclear cogeneration can be accelerated if several issues between vendors and users involved in such projects are properly identified and addressed. This includes formulating suitable business models for the project, infrastructure development, and managing stakeholders’ involvement. The stakeholders in cogeneration projects include the utilities for electricity, heat, water and others, the regulatory bodies, and suppliers and contractors for nuclear and the industrial processes, along with the general public and other commercial and industrial users of the cogenerated products and services. Public acceptance is expected to play a major role in the successful deployment of nuclear cogeneration projects. Stakeholders' interaction at the pre-project stage can include activities related to siting, target utilization of the product or cogenerated commodity, status and selection of technologies for the coupled industrial plant, regulatory implications and financial constraints, and considerations among other elements. This paper assesses some of these issues with a focus on nuclear cogeneration for seawater desalination. It also discusses the licensing consideration and issues, and proposes a licensing scheme for nuclear cogeneration projects.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.924
Threshold uncertainty score0.331

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 communication0.0000.000
Open science0.0000.000
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 head0.033
GPT teacher head0.259
Teacher spread0.226 · 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