{"id":"W3213111692","doi":"10.1016/j.ifacol.2021.08.024","title":"Properties of Metal Extrusion Additive Manufacturing and Its Application in Digital Supply Chain Management","year":2021,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Additive Manufacturing and 3D Printing Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ontario Institute of Technology","funders":"","keywords":"Manufacturing engineering; Supply chain; Production (economics); Discrete manufacturing; Product (mathematics); Digital manufacturing; Computer science; Digital economy; Product lifecycle; 3D printing; Business; Industrial organization; Industrial engineering; Commerce; New product development; Mechanical engineering; Engineering; Economics; Marketing; Mathematics; Microeconomics","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":[],"consensus_categories":[],"category_scores_codex":[0.00006190363,0.0001722228,0.0002090406,0.0001283905,0.00003569902,0.00002305612,0.0001083884,0.00007605199,0.00002015553],"category_scores_gemma":[0.00003574293,0.0001599738,0.00004014975,0.0001033871,0.00004530036,0.0001552646,0.0001751871,0.0001624362,0.000009215671],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004095514,"about_ca_system_score_gemma":0.000005169078,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005708316,"about_ca_topic_score_gemma":0.00001666436,"domain_scores_codex":[0.9991951,0.000009157359,0.0002119987,0.0002511316,0.0001294488,0.0002031723],"domain_scores_gemma":[0.9997119,0.0000274504,0.00003886815,0.0001676534,0.00002600155,0.00002812145],"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":[0.00004006646,0.0001498994,0.0003835512,0.000856804,0.0002055812,0.0001064389,0.0009042791,0.001893717,0.1204718,0.00129868,0.000005500957,0.8736836],"study_design_scores_gemma":[0.0003284534,0.00001860184,0.005710596,0.0001963129,0.00001573258,0.00001227283,0.001058774,0.005794664,0.9859028,0.0002483029,0.0005027167,0.0002107742],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9967369,0.0008620199,0.0006534675,0.0001181434,0.00004729685,0.0002092662,0.00008483126,0.0002831849,0.001004895],"genre_scores_gemma":[0.9920582,0.0004558944,0.007100626,0.000009992627,0.00002854289,0.00004467276,0.00006603535,0.00002376417,0.0002122982],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8734729,"threshold_uncertainty_score":0.6523541,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01065757719710844,"score_gpt":0.1987017009643007,"score_spread":0.1880441237671923,"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."}}