Data-Theoretic: DT-BASE - Training Quality Causal Model

Dataset

Description

Dataset includes structure and values of a causal model for Training Quality in nuclear power plants. Each entry refers to a piece of evidence supporting causality of the Training Quality causal model. Includes bibliographic information, context-specific text from the reference, and three weighted values; (M1) credibility of reference, (2) causality determined by the author, and (3) analysts confidence level.

(M1, M2, and M3) Weight metadata are based on probability language from: Intergovernmental Panel on Climate Change (IPCC), Climate Change 2001: Synthesis Report. The URL to the report: https://www.ipcc.ch/ipccreports/tar/vol4/english/index.htm. The language can be found firstly in “Summary for Policymakers” section, in PDF format.

Weight Metadata:
LowerBound_Probability, UpperBound_Probability, Qualitative Language
0.99, 1, Virtually Certain
0.9, 0.99, Very Likely
0.66, 0.9, Likely
0.33, 0.66, Medium Likelihood
0.1, 0.33, Unlikely
0.01, 0.1, Very Unlikely
0, 0.01, Extremely Unlikely

Date made availableDec 15 2017
PublisherUniversity of Illinois at Urbana-Champaign

Keywords

  • Data-Theoretic
  • DT-BASE
  • Organization
  • Theory-Building
  • Bayesian Network
  • Training
  • Causal Model
  • Training Quality
  • Bayesian Belief Network
  • Probabilistic Risk Assessment

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