{"id":"W4403489415","doi":"10.46939/j.sci.arts-24.3-c02","title":"ASSESSMENT OF DELAYED HYDRIDE CRACKING IN CANDU PRESSURE TUBE USING THE PROCESS ZONE WITH CREEP EQUATION FROM ARTIFICIAL NEURAL NETWORK MODELLING","year":2024,"lang":"en","type":"article","venue":"Journal of Science and Arts","topic":"Nuclear Materials and Properties","field":"Materials Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Creep; Cracking; Artificial neural network; Hydride; Tube (container); Process (computing); Materials science; Metallurgy; Composite material; Computer science; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001654229,0.00006776851,0.0001477255,0.0000652309,0.000209586,0.000380351,0.0001615081,0.00001878776,0.00002521809],"category_scores_gemma":[0.00001801933,0.00003599329,0.00001458484,0.0002391634,0.0002871261,0.0008314668,0.00003596302,0.00009812547,2.318702e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002025763,"about_ca_system_score_gemma":0.0002333685,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003034857,"about_ca_topic_score_gemma":0.00005336981,"domain_scores_codex":[0.9988643,0.00003417807,0.0003140893,0.0001328994,0.0004904285,0.0001641291],"domain_scores_gemma":[0.999505,0.00004940109,0.0001753365,0.00006527694,0.0001675284,0.00003740317],"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":[0.00005033518,0.000008912244,0.0002782239,0.0000198878,0.000003844882,0.0000092762,0.001223424,0.5249189,0.4727252,0.0001915337,0.000003442975,0.0005670866],"study_design_scores_gemma":[0.00008153167,0.0001254099,0.0007356676,0.0004319937,0.0000348717,0.00002778223,0.0005910273,0.9549261,0.04144618,0.001484159,0.00004771353,0.00006757911],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9917767,0.0005609107,0.006950312,0.0002037366,0.0004055311,0.00006920133,0.000002806391,0.000004451303,0.00002634163],"genre_scores_gemma":[0.9974316,0.00001945557,0.002258509,0.00003649563,0.0002451616,7.170937e-7,1.329253e-7,0.000005418127,0.000002514101],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.431279,"threshold_uncertainty_score":0.3667733,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05456296947176215,"score_gpt":0.2957970111049719,"score_spread":0.2412340416332097,"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."}}