{"id":"W2966251892","doi":"10.12943/cnr.2019.00006","title":"RESULTS OF A PHENOMENA IDENTIFICATION AND RANKING TABLE (PIRT) EXERCISE FOR A SEVERE ACCIDENT IN A SMALL MODULAR HIGH-TEMPERATURE GAS-COOLED REACTOR","year":2019,"lang":"en","type":"article","venue":"CNL Nuclear Review","topic":"Graphite, nuclear technology, radiation studies","field":"Materials Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Nuclear Laboratories","funders":"","keywords":"Ranking (information retrieval); Modular design; Identification (biology); Process (computing); Limiting; Table (database); Computer science; Nuclear engineering; Risk analysis (engineering); Operations research; Engineering; Data mining; Mechanical engineering; Business; Machine learning","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009161784,0.0005832409,0.0006917241,0.003445997,0.002070789,0.001383549,0.0007527615,0.0007782703,0.01196076],"category_scores_gemma":[0.02283304,0.0001744568,0.001248439,0.001721502,0.000417498,0.0007725941,0.00086435,0.0007200093,0.002010845],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003254338,"about_ca_system_score_gemma":0.003625475,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02633835,"about_ca_topic_score_gemma":0.07613244,"domain_scores_codex":[0.9953762,0.001343619,0.0003315365,0.0005295154,0.002128354,0.0002906673],"domain_scores_gemma":[0.9693991,0.01801319,0.00204242,0.001084169,0.008912794,0.0005482576],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.002877881,0.001507656,0.07063497,0.003964784,0.0004192163,0.004133955,0.01177076,0.08403458,0.03031505,0.01360091,0.1896388,0.5871016],"study_design_scores_gemma":[0.0004114441,0.003899268,0.3058431,0.001196775,0.0008143457,0.001999259,0.03996063,0.2082892,0.06890235,0.01442918,0.3535832,0.0006712719],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7397272,0.001087935,0.1158844,0.006543795,0.0006307583,0.004938079,0.02130078,0.003598355,0.1062887],"genre_scores_gemma":[0.8043,0.0009627002,0.1450606,0.0005846606,0.0001600103,0.0007800155,0.01621651,0.0003423656,0.03159313],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02633835,"threshold_uncertainty_score":0.05237007,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01148530715481013,"score_gpt":0.2290338650849445,"score_spread":0.2175485579301344,"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."}}