{"id":"W4294843302","doi":"10.1016/j.jmrt.2022.08.169","title":"Wire and arc additive manufacturing of 316L stainless steel/Inconel 625 functionally graded material: development and characterization","year":2022,"lang":"en","type":"article","venue":"Journal of Materials Research and Technology","topic":"Additive Manufacturing Materials and Processes","field":"Engineering","cited_by":206,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Fundação para a Ciência e a Tecnologia; European Institute of Innovation and Technology; UNIDEMI; Deutsches Elektronen-Synchrotron; Ministério da Ciência, Tecnologia e Ensino Superior; European Commission; Horizon 2020; China Scholarship Council; CALIPSOplus","keywords":"Materials science; Inconel 625; Metallurgy; Characterization (materials science); Inconel; Microstructure; Nanotechnology; Alloy","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0007657286,0.0001461975,0.0003709049,0.0005937501,0.000233878,0.00008641545,0.0001395057,0.00009201923,0.0002856881],"category_scores_gemma":[0.00004178327,0.0001304881,0.000010256,0.0001111838,0.0002145274,0.0001684453,0.0003035276,0.0002268928,5.931188e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006054441,"about_ca_system_score_gemma":0.00006445406,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000437645,"about_ca_topic_score_gemma":0.000001861317,"domain_scores_codex":[0.9987183,0.00009943268,0.0004659067,0.0001557182,0.000295691,0.0002650066],"domain_scores_gemma":[0.9994054,0.00008003268,0.000190878,0.00008059262,0.0001748611,0.00006825947],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003114568,0.00003690493,0.00007733869,0.0005057887,0.0001041979,0.00005006753,0.0002674072,0.000007683867,0.9869759,0.0005687636,0.00006101456,0.01103346],"study_design_scores_gemma":[0.0004305331,0.0003662807,0.008350495,0.00007428508,0.000008435247,0.0002442259,0.0006073298,0.000003976496,0.9805971,0.002924606,0.006270243,0.0001225266],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9987577,0.0002033792,0.00009048473,0.0001723241,0.0002869877,0.0001593055,0.0002719294,0.00003771875,0.00002014984],"genre_scores_gemma":[0.9987319,0.0007495601,0.0002733333,0.000006446609,0.00008834171,0.00003538028,0.0000493674,0.0000249148,0.0000408247],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01091093,"threshold_uncertainty_score":0.532115,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02138423552377826,"score_gpt":0.2462437722666279,"score_spread":0.2248595367428497,"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."}}