{"id":"W3049658623","doi":"10.1016/j.msea.2020.140103","title":"Strength prediction of Ni-base disc superalloys: Modified γ′ hardening models applicable to commercial alloys","year":2020,"lang":"en","type":"article","venue":"Materials Science and Engineering A","topic":"High Temperature Alloys and Creep","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Council for Science, Technology and Innovation; Swine Innovation Porc","keywords":"Superalloy; Materials science; Hardening (computing); Predictability; Precipitation hardening; Alloy; Volume fraction; Work hardening; Exponential function; Nonlinear system; Mechanics; Metallurgy; Structural engineering; Composite material; Mathematics; Engineering; Microstructure; Mathematical analysis; Physics; Statistics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003149771,0.0004893216,0.0004001033,0.0003496309,0.0001909479,0.0003947739,0.001465788,0.0007165355,0.001347295],"category_scores_gemma":[0.0006531554,0.0002620526,0.0003790137,0.0002850295,0.0002382689,0.0003647479,0.0002335305,0.0003119698,0.0003673275],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006183235,"about_ca_system_score_gemma":0.0005045963,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01196981,"about_ca_topic_score_gemma":0.01158098,"domain_scores_codex":[0.9999301,0.00001111798,0.000004352748,0.00001305387,0.00003046225,0.0000107447],"domain_scores_gemma":[0.9997389,0.00008051118,0.0000301576,0.00003076862,0.0001040967,0.00001562829],"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":[0.0001250049,0.00005161044,0.001329073,0.00006987035,0.00001646507,0.00007918505,0.00002900478,0.95537,0.02954184,0.001456174,0.0004509258,0.01148088],"study_design_scores_gemma":[0.000003827563,0.00001679799,0.0004416447,0.000001738878,0.000002880667,0.000005288353,0.000004619645,0.995934,0.003310388,0.0001301127,0.000146312,0.000002317297],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8898776,0.001015465,0.08983872,0.0001934021,0.00006328123,0.0000724373,0.0004197446,0.0005010688,0.01801831],"genre_scores_gemma":[0.9934013,0.000127922,0.003672088,0.00001096894,0.000005871112,0.00001399208,0.00008769285,0.00004452444,0.00263546],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01196981,"threshold_uncertainty_score":0.02380025,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01547330637950444,"score_gpt":0.1863145400417721,"score_spread":0.1708412336622676,"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."}}