{"id":"W2077312099","doi":"10.1016/j.nucengdes.2010.05.037","title":"A probabilistic approach to update lower bound threshold stress intensity factor (K) for delayed hydride cracking","year":2010,"lang":"en","type":"article","venue":"Nuclear Engineering and Design","topic":"Risk and Safety Analysis","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Atomic Energy (Canada); University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; University Network of Excellence in Nuclear Engineering","keywords":"Probabilistic logic; Fracture toughness; Cracking; Upper and lower bounds; Stress intensity factor; Materials science; Structural engineering; Fracture (geology); Margin (machine learning); Reactor pressure vessel; Reliability engineering; Hydride; Forensic engineering; Fracture mechanics; Nuclear engineering; Computer science; Engineering; Composite material; Metallurgy; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0009952883,0.0001862546,0.0003347091,0.0002017551,0.0001945633,0.0004284373,0.0003765477,0.0001141915,0.00004652074],"category_scores_gemma":[0.001423964,0.0001449656,0.0001210064,0.0003544756,0.00004256762,0.0001888693,0.00008863393,0.0002449213,0.00005178702],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001673982,"about_ca_system_score_gemma":0.00001795075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001550804,"about_ca_topic_score_gemma":0.00001077758,"domain_scores_codex":[0.998404,0.00002223734,0.0003429824,0.0005082346,0.0004091345,0.0003134734],"domain_scores_gemma":[0.998607,0.000416537,0.00006396879,0.0004745378,0.0002075622,0.0002304294],"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":[0.001324795,0.0004429002,0.0008702372,0.00009403565,0.0003472319,0.00002720996,0.004237873,0.8617404,0.04084711,0.02245238,0.007275145,0.06034065],"study_design_scores_gemma":[0.0002573606,0.0001096303,0.002308447,0.00001305152,0.00004133811,0.00001083267,0.0001181156,0.9831264,0.0003814706,0.004679462,0.008638609,0.0003152681],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5760266,0.0000245774,0.4227623,0.0002534382,0.0003150552,0.0003237289,0.00003016219,0.000101653,0.0001625211],"genre_scores_gemma":[0.9482397,0.000007003025,0.05141739,0.0001062422,0.00009776017,0.00001381557,0.000002172777,0.00002997943,0.00008588175],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3722132,"threshold_uncertainty_score":0.5911524,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05252645955931463,"score_gpt":0.2841052604053622,"score_spread":0.2315788008460476,"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."}}