{"id":"W4313855446","doi":"10.22541/au.167330563.32218729/v1","title":"GENERAL INDUSTRIAL PROCESS OPTIMIZATION METHOD TO LEVERAGE MACHINE LEARNING APPLIED TO INJECTION MOLDING","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Injection Molding Process and Properties","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick; Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; New Brunswick Innovation Foundation","keywords":"Automation; Leverage (statistics); Manufacturing engineering; Industrial engineering; Computer science; Generality; Artificial intelligence; Process (computing); Industrial production; Context (archaeology); Machine learning; Engineering; Mechanical engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0008267975,0.0008622795,0.0007043244,0.0006004145,0.0003006132,0.0006385086,0.0009866129,0.001023915,0.003455625],"category_scores_gemma":[0.001122652,0.0004191867,0.0009393167,0.0005294926,0.0004037973,0.0005005954,0.0008532912,0.001013142,0.0007651803],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005811889,"about_ca_system_score_gemma":0.001215811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003581387,"about_ca_topic_score_gemma":0.003640761,"domain_scores_codex":[0.9996831,0.00006664839,0.00001874406,0.00007438959,0.0001307295,0.00002635506],"domain_scores_gemma":[0.999787,0.00008555259,0.00002601839,0.00002457056,0.00006803307,0.000008834062],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003343654,0.00006596166,0.0004726913,0.0001397052,0.00003294913,0.00007537916,0.00003461667,0.8945951,0.007406243,0.009045482,0.0009791339,0.08711928],"study_design_scores_gemma":[0.000002986758,0.00001048023,0.00004974361,0.000003321018,0.000002645026,0.000008096912,0.000002016948,0.9968055,0.0009263373,0.001217932,0.0009688255,0.000002050276],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003022659,0.000106398,0.9941723,0.0000524864,0.00001711858,0.00005340602,0.00002970132,0.0003420915,0.002203838],"genre_scores_gemma":[0.2488524,0.0002724213,0.7420325,0.0001140769,0.00005804394,0.000409161,0.0002620143,0.0003272539,0.007672161],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003581387,"threshold_uncertainty_score":0.01156026,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0635212351000316,"score_gpt":0.2906300224789859,"score_spread":0.2271087873789543,"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."}}