{"id":"W1989086382","doi":"10.1115/1.1310164","title":"Fitting Creep-Rupture Life Distribution Using Accelerated Life Testing Data","year":2000,"lang":"en","type":"article","venue":"Journal of Pressure Vessel Technology","topic":"Fatigue and fracture mechanics","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Syncrude (Canada); University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Syncrude","keywords":"Creep; Log-normal distribution; Least-squares function approximation; Applied mathematics; Distribution (mathematics); Computer science; Statistics; Mathematics; Materials science; Mathematical analysis; Composite material","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005474685,0.0008622014,0.0007339134,0.001991479,0.0004650031,0.0007670808,0.001480733,0.001191245,0.001832735],"category_scores_gemma":[0.0200413,0.0002834579,0.0009935943,0.001384272,0.0003062627,0.001450263,0.0007027026,0.001179355,0.001215395],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005690405,"about_ca_system_score_gemma":0.0005375509,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002853169,"about_ca_topic_score_gemma":0.003253945,"domain_scores_codex":[0.9979295,0.0007043118,0.0002219355,0.0003290732,0.0007176729,0.00009750595],"domain_scores_gemma":[0.992528,0.003779334,0.0006859046,0.001030548,0.001895603,0.00008068277],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004227699,0.0005558854,0.079435,0.000532335,0.000179589,0.0008212485,0.001162664,0.5873586,0.04488838,0.01132664,0.006280571,0.2670363],"study_design_scores_gemma":[0.00004116092,0.0008104797,0.032029,0.00009945808,0.00004560951,0.0009664354,0.0003797745,0.9108399,0.02829793,0.01372505,0.01253346,0.0002317368],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2968418,0.0002996715,0.6949996,0.0003149188,0.00009400015,0.0002441969,0.00118812,0.002402551,0.003615132],"genre_scores_gemma":[0.808854,0.0004158831,0.1830429,0.0002175673,0.00005221726,0.0003756639,0.003041478,0.0008619032,0.003138376],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005474685,"threshold_uncertainty_score":0.02895325,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08531950284687165,"score_gpt":0.2874780684610124,"score_spread":0.2021585656141407,"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."}}