{"id":"W2155213674","doi":"10.1002/qre.1861","title":"An Efficient Approximate Markov Chain Method in Dynamic Fault Tree Analysis","year":2015,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Risk and Safety Analysis","field":"Decision Sciences","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Health Services and Policy Research","funders":"","keywords":"Markov chain; Fault tree analysis; Truncation (statistics); Markov model; Computer science; Markov process; Transformation (genetics); Chain (unit); Mathematics; Tree (set theory); Mathematical optimization; Algorithm; Applied mathematics; Reliability engineering; Statistics; Engineering; Combinatorics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01458365,0.0001783181,0.0005046209,0.0008415033,0.00004838155,0.0001774076,0.0006287884,0.0001237175,0.00007543556],"category_scores_gemma":[0.00392899,0.000141349,0.0002362473,0.001504857,0.00005738127,0.0002243218,0.0001154964,0.0002321009,0.00001223739],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001864726,"about_ca_system_score_gemma":0.00004129647,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005014978,"about_ca_topic_score_gemma":0.0003049028,"domain_scores_codex":[0.9961679,0.0005422804,0.001024112,0.0007082158,0.001324113,0.0002333892],"domain_scores_gemma":[0.9976214,0.0009792278,0.0001692427,0.0006419418,0.0003580143,0.0002301348],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006021536,0.0001668276,0.02462303,0.000006062456,0.00009760484,0.000002959432,0.0009270281,0.9585387,0.0000764785,0.002116445,0.00001307768,0.01337159],"study_design_scores_gemma":[0.0002923716,0.00001952558,0.1761817,0.000004105071,0.00003934717,0.000001600457,0.0005038136,0.8182054,0.00001766573,0.00418078,0.0004050526,0.0001486899],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5047756,0.00004285583,0.4925351,0.00188674,0.0002232571,0.00007621959,0.00003635968,0.00004166521,0.0003821732],"genre_scores_gemma":[0.9591197,0.00002062566,0.04049378,0.0000496819,0.00003578914,0.00001292873,0.0000424185,0.000007239251,0.0002178079],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4543442,"threshold_uncertainty_score":0.5764046,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05212734067100711,"score_gpt":0.4113555825427406,"score_spread":0.3592282418717335,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}