{"id":"W4385078283","doi":"10.18280/isi.280301","title":"Exploring Machine Learning Tools for Enhancing Additive Manufacturing: A Comparative Study","year":2023,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Additive Manufacturing and 3D Printing Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Manufacturing engineering; Computer science; Artificial intelligence; Machine learning; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001905041,0.0005767659,0.0006606108,0.002357973,0.0002513528,0.001839838,0.000627096,0.0007775431,0.00205883],"category_scores_gemma":[0.003300783,0.0001944605,0.0008756875,0.002344262,0.0003760983,0.001527033,0.0004923316,0.0005701295,0.0005360931],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000648329,"about_ca_system_score_gemma":0.000560609,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008240349,"about_ca_topic_score_gemma":0.001300254,"domain_scores_codex":[0.9992489,0.0002091125,0.00006091496,0.00009679695,0.0003409193,0.00004333042],"domain_scores_gemma":[0.9981383,0.001321734,0.0001093324,0.00006022124,0.000327702,0.00004267321],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002988649,0.0003216995,0.003068103,0.006342063,0.000240722,0.0001960381,0.0004529646,0.009117676,0.003389098,0.01087532,0.002046097,0.9636512],"study_design_scores_gemma":[0.0002014963,0.01081763,0.06570463,0.01505176,0.002754019,0.00375166,0.004479098,0.1694491,0.05214561,0.03087548,0.6443859,0.0003836648],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.1317822,0.7550527,0.04897102,0.001756348,0.0002948824,0.0002059673,0.0001423962,0.0002024518,0.06159205],"genre_scores_gemma":[0.4065984,0.5328355,0.05144442,0.0004693315,0.0002745189,0.0001063535,0.0002307274,0.00006098987,0.007979702],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002357973,"threshold_uncertainty_score":0.01007491,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08314143940373113,"score_gpt":0.2634857014947281,"score_spread":0.1803442620909969,"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."}}