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Record W1976125743 · doi:10.2991/jrarc.2014.4.4.1

A Two-Step Water-Management Approach for Nuclear Power Plants in Inland China

2014· article· en· W1976125743 on OpenAlexaff
Xiaowen Ding, Wei Wang, Guohe Huang, Qingwei Chen, Guoliang Wei

Bibliographic record

VenueJournal of risk analysis and crisis response · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicWater-Energy-Food Nexus Studies
Canadian institutionsUniversity of Regina
FundersFundamental Research Funds for the Central UniversitiesNational Development and Reform CommissionMinistry of Water ResourcesU.S. Nuclear Regulatory Commission
KeywordsChinaNuclear powerNuclear power plantEnvironmental scienceWater resource managementBusinessNuclear engineeringEngineeringGeographyBiologyEcologyPhysicsNuclear physics

Abstract

fetched live from OpenAlex

Nowadays, effective management of water withdraw, water consumption and wastewater discharge is desired for nuclear power plants in inland China. In this paper, the inland nuclear power industry and its policies in China were reviewed, a two-step water-management (TSWM) approach for the plants was proposed. The framework includes the flow process, main tasks, and tools of TSWM management for any nuclear power plant in inland China. Finally, suggestions on future development of the management were also put forward.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.330
Threshold uncertainty score0.411

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.004
GPT teacher head0.208
Teacher spread0.204 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations3
Published2014
Admission routes1
Has abstractyes

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