{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009532433,0.0001894479,0.0006678758,0.000161738,0.00007132221,0.00006027643,0.0003041213,0.0001210679,0.00009036975],"category_scores_gemma":[0.0003310312,0.0001716599,0.00007696584,0.0003718712,0.00007423536,0.0001971862,0.0001349848,0.0001277401,0.0001125874],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005915116,"about_ca_system_score_gemma":0.00002006112,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003875507,"about_ca_topic_score_gemma":0.00001854063,"domain_scores_codex":[0.9983197,0.00007422535,0.000678605,0.0005106946,0.0001561279,0.0002606222],"domain_scores_gemma":[0.9987214,0.00006898579,0.0004433148,0.0005985987,0.0001239746,0.00004378076],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003578108,0.0001665153,0.0006345696,0.006333085,0.00004199816,0.000003669588,0.001001668,0.00001680245,0.9718364,0.003323942,0.00415617,0.01212738],"study_design_scores_gemma":[0.04221017,0.002088194,0.1781906,0.09856647,0.002136259,0.0001899695,0.004495284,0.002385418,0.1113203,0.0178207,0.5342432,0.006353394],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9690641,0.02799756,0.000001518746,0.0007651749,0.0001555304,0.001798767,0.00006884402,0.00009275899,0.00005572462],"genre_scores_gemma":[0.9663476,0.03224313,0.0008909033,0.0002182782,0.00002206724,0.00008213087,0.00002296214,0.00004399193,0.0001289554],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8605161,"threshold_uncertainty_score":0.7000087,"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."}}