{"id":"W4320014274","doi":"10.1088/1757-899x/1266/1/011001","title":"Preface","year":2023,"lang":"en","type":"article","venue":"IOP Conference Series Materials Science and Engineering","topic":"Additive Manufacturing and 3D Printing Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Globe; Personalization; Mass customization; Clothing; Table of contents; Textile; Table (database); Political science; Business; Management; Public relations; Library science; Engineering; Engineering management; Marketing; World Wide Web; Computer science; History; Psychology; Economics","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":[0.0003325197,0.0001514231,0.0001487089,0.0002202775,0.0001315046,0.0002537354,0.0002734818,0.00005155338,0.00004128299],"category_scores_gemma":[0.0001589676,0.0001434951,0.000009333676,0.0004483297,0.0002374389,0.0004566482,0.0001850157,0.00007953784,0.00007412105],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002643685,"about_ca_system_score_gemma":0.00002350879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005082975,"about_ca_topic_score_gemma":7.91442e-7,"domain_scores_codex":[0.9990613,0.000003108864,0.0001326317,0.0002088359,0.0001813845,0.000412683],"domain_scores_gemma":[0.9996556,0.00001961391,0.00001383591,0.0001928471,0.00005559107,0.00006250617],"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":[0.000001542432,9.56196e-7,0.000007437344,0.00009064049,0.000004827269,0.000005862686,0.0002030484,0.002624132,0.9875211,0.003998859,0.0001943556,0.005347254],"study_design_scores_gemma":[0.0000432363,0.00001743417,0.005519211,0.00004666853,0.000002369505,0.0000114721,0.0001857726,0.003718292,0.9864448,0.0003807965,0.003425989,0.0002039392],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9947233,0.00002035609,0.0003959334,0.000117218,0.0006151108,0.00007075489,0.00001268298,0.003142032,0.0009026634],"genre_scores_gemma":[0.9988301,0.0002089336,0.0007664565,0.00000523894,0.0000423789,0.00002401713,0.000003049242,0.00001767898,0.0001021736],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005511774,"threshold_uncertainty_score":0.5851561,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01843060008828769,"score_gpt":0.2119790310981708,"score_spread":0.1935484310098831,"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."}}