{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00008554406,0.0003292075,0.000366777,0.0001197354,0.00005708762,0.00008609338,0.0002359399,0.0001308199,0.0001887845],"category_scores_gemma":[0.00002658459,0.0003359162,0.00005527942,0.0000806387,0.00002157938,0.0002254422,0.00003931156,0.00008071196,0.000126574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006765896,"about_ca_system_score_gemma":0.000009806266,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001200989,"about_ca_topic_score_gemma":4.450256e-7,"domain_scores_codex":[0.9988802,0.000005511517,0.000270798,0.0003097053,0.000100906,0.0004328811],"domain_scores_gemma":[0.9993547,0.0001706577,0.00005835012,0.0003329549,0.0000396817,0.00004370475],"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":[0.00002361692,0.00001212253,0.00000197603,0.001005875,0.00004657119,0.000001314301,0.00001672041,0.2664795,0.7166746,0.0003126276,0.0001682015,0.0152569],"study_design_scores_gemma":[0.0004254362,0.00003865955,0.00009080023,0.00009349366,0.00001308099,0.000001804701,0.00002922503,0.001175325,0.9514108,0.0003249223,0.04599643,0.0004000563],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.756385,0.0002366192,0.2328549,0.0000464314,0.001526465,0.001998044,0.00128478,0.005257637,0.0004101658],"genre_scores_gemma":[0.9774897,0.00007346897,0.02039236,0.00001793055,0.0001179739,0.00149374,0.0002076504,0.000118374,0.00008886185],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2653041,"threshold_uncertainty_score":0.9999093,"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."}}