{"id":"W4200068761","doi":"10.37775/eis.2021.2.1","title":"Application of 3D printing in casting","year":2021,"lang":"en","type":"article","venue":"Mérnöki és Informatikai Megoldások","topic":"Additive Manufacturing and 3D Printing Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Savaria (Canada)","funders":"Nemzeti Kutatási, Fejlesztési és Innovaciós Alap","keywords":"3D printing; Infill; Casting; Investment casting; Sand casting; Engineering drawing; Process (computing); Materials science; Gypsum; Mechanical engineering; Deformation (meteorology); Computer science; Mold; Engineering; Composite material; Structural 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":[0.0005243362,0.0004357537,0.0005663626,0.001169232,0.0005803339,0.001892234,0.0006247483,0.001056217,0.004632529],"category_scores_gemma":[0.0008487717,0.0004516786,0.00107822,0.0009892555,0.001031292,0.0005706131,0.001582223,0.0008612421,0.002456187],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007070903,"about_ca_system_score_gemma":0.0005617756,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001343764,"about_ca_topic_score_gemma":0.001240925,"domain_scores_codex":[0.99873,0.0001429305,0.0000552369,0.000130944,0.000856883,0.0000839887],"domain_scores_gemma":[0.9994984,0.000153225,0.00002961343,0.0001856927,0.0001073095,0.00002572847],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001344307,0.00008629046,0.001682565,0.001437572,0.00006410068,0.001384414,0.0005988891,0.02746403,0.3800008,0.07264505,0.00550751,0.5089944],"study_design_scores_gemma":[0.00002825703,0.000231289,0.003756008,0.0004186537,0.00007119343,0.005249258,0.0001639238,0.06125793,0.5309161,0.02662893,0.3711037,0.0001747247],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05453632,0.01811945,0.8047069,0.001074725,0.001446743,0.0001590795,0.0004368596,0.002715684,0.1168042],"genre_scores_gemma":[0.5358859,0.02278203,0.4086402,0.0006453678,0.0004046554,0.0001464524,0.0005112542,0.0005289125,0.03045519],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004632529,"threshold_uncertainty_score":0.01549739,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009443910116310862,"score_gpt":0.2118261909161244,"score_spread":0.2023822807998136,"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."}}