{"id":"W2885694319","doi":"10.1007/s00170-018-2421-z","title":"The use of nano-computed tomography (nano-CT) in non-destructive testing of metallic parts made by laser powder-bed fusion additive manufacturing","year":2018,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Additive Manufacturing Materials and Processes","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Nano-; Materials science; Nanoscopic scale; Characterization (materials science); Fusion; Durability; Computed tomography; Tomography; Aluminium; Nanotechnology; Composite material; Optics; Radiology; Medicine","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":[0.0002785726,0.0002959973,0.0004498236,0.0005324861,0.0001185328,0.00005362678,0.001044501,0.00009999429,0.00003660403],"category_scores_gemma":[0.0002279659,0.0001998175,0.0001195772,0.0002058506,0.0004670603,0.0003934597,0.000265314,0.000407934,0.00000229224],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001060923,"about_ca_system_score_gemma":0.00003036536,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000344489,"about_ca_topic_score_gemma":0.00003251279,"domain_scores_codex":[0.9981193,0.00004982472,0.0008682896,0.0002089056,0.0004103364,0.0003433735],"domain_scores_gemma":[0.9977678,0.0007489441,0.0008098714,0.000279967,0.0003498212,0.00004357636],"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":[0.0006063649,0.0001126486,0.0002791395,0.0001009231,0.0008552027,0.00007234312,0.0004154033,0.02095034,0.8718598,0.000240844,0.0005100213,0.103997],"study_design_scores_gemma":[0.0006258422,0.0001862119,0.002360909,0.0003550616,0.00002988971,0.000139188,0.0001862685,0.0001172312,0.9837625,0.008153656,0.003899584,0.0001836441],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973792,0.0001831159,0.0008933809,0.0002485428,0.0008783534,0.0001982646,0.00007793314,0.00007432436,0.00006683901],"genre_scores_gemma":[0.9956064,0.0002926812,0.003850007,0.00003106922,0.0001407278,0.00001291216,0.000007589172,0.00004107275,0.00001755504],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1119027,"threshold_uncertainty_score":0.8148319,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01138135953802774,"score_gpt":0.2257746060381494,"score_spread":0.2143932465001217,"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."}}