{"id":"W2969593459","doi":"10.1002/adem.201900617","title":"Laser‐Based Additive Manufacturing Technologies for Aerospace Applications","year":2019,"lang":"en","type":"article","venue":"Advanced Engineering Materials","topic":"Additive Manufacturing Materials and Processes","field":"Engineering","cited_by":188,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; Carleton University","funders":"","keywords":"Aerospace; Automotive industry; Materials science; Manufacturing engineering; Aerospace materials; Process (computing); Mechanical engineering; Systems engineering; Aerospace engineering; Computer science; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004965748,0.0005812656,0.0004503982,0.001438998,0.0005992688,0.001221739,0.0007791953,0.0009568978,0.006460227],"category_scores_gemma":[0.0004559605,0.0003937099,0.0005201632,0.001334479,0.000366384,0.001063794,0.0008115024,0.001816697,0.0054068],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006884086,"about_ca_system_score_gemma":0.0006321447,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004108998,"about_ca_topic_score_gemma":0.0009477508,"domain_scores_codex":[0.9992766,0.00004420994,0.00004438349,0.00006543123,0.0005275993,0.00004175213],"domain_scores_gemma":[0.9997134,0.000058074,0.00005588689,0.0000367593,0.000119069,0.00001678559],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004530153,0.0001156734,0.0003517464,0.004165218,0.00004202857,0.0005964212,0.0002264785,0.002540672,0.4453488,0.04427426,0.02068921,0.4816042],"study_design_scores_gemma":[0.0000093709,0.0002186258,0.0007305138,0.0004121032,0.00003625441,0.0017469,0.00006013443,0.002545407,0.1860952,0.007227813,0.8008644,0.00005325705],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.02533252,0.4727291,0.3175021,0.003010226,0.0039976,0.0004409183,0.0008225209,0.001617754,0.1745473],"genre_scores_gemma":[0.2032654,0.4133052,0.2993205,0.001706021,0.001835973,0.0005272416,0.001116768,0.0003122753,0.07861074],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.006460227,"threshold_uncertainty_score":0.02161157,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003998050297144638,"score_gpt":0.1967769408269655,"score_spread":0.1927788905298208,"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."}}