{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006315687,0.0004344952,0.0004312551,0.0006786741,0.0002205354,0.0003045734,0.000266421,0.0005085773,0.000251215],"category_scores_gemma":[0.0001005791,0.0004388376,0.00007989458,0.0007211747,0.000005852781,0.00009991279,0.0003211699,0.001174022,0.00007940904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002524255,"about_ca_system_score_gemma":0.00006329447,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004241674,"about_ca_topic_score_gemma":0.00003785335,"domain_scores_codex":[0.9981114,0.00006429034,0.0004764634,0.0006108739,0.0003341364,0.0004028743],"domain_scores_gemma":[0.9993703,0.00003683032,0.0000744717,0.0002236022,0.0001322823,0.0001625232],"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":[0.00005248997,0.000006346403,0.00006340008,0.0002936545,0.00006553654,0.000001207351,0.001229306,0.9860579,0.0006217165,0.00004688645,0.0008012269,0.01076028],"study_design_scores_gemma":[0.0002396604,0.00007526748,0.00001564045,0.0001708664,0.00003591497,0.000004721208,0.0001726915,0.981185,0.01537731,0.0001437476,0.001907477,0.0006716549],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02669526,0.00004065564,0.9644935,0.0001114594,0.002190069,0.0008774055,0.00001347126,0.002667631,0.002910561],"genre_scores_gemma":[0.8160313,0.0000400124,0.1719529,0.0002607292,0.002182121,0.001145004,0.0002247284,0.0004225689,0.007740668],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7925406,"threshold_uncertainty_score":0.9998063,"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."}}