{"id":"W4409702504","doi":"10.3390/molecules30091879","title":"Implementation of Machine Learning in Flat Die Extrusion of Polymers","year":2025,"lang":"en","type":"article","venue":"Molecules","topic":"Rheology and Fluid Dynamics Studies","field":"Chemical Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"European Commission","keywords":"Extrusion; Rheology; Die (integrated circuit); Throughput; Polymer; Shear rate; Computer science; Support vector machine; Materials science; Artificial intelligence; Volumetric flow rate; Mechanical engineering; Machine learning; Composite material; Engineering drawing; Nanotechnology; Mechanics; Engineering; Physics","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":[0.001113882,0.0005579447,0.0004755038,0.0003769764,0.0002999447,0.0005801589,0.0009800331,0.0009777998,0.001498631],"category_scores_gemma":[0.002764378,0.0002727799,0.0005221387,0.0003180954,0.0003611975,0.0005200186,0.0004391791,0.0007032771,0.0004385083],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006025037,"about_ca_system_score_gemma":0.0008561686,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004846775,"about_ca_topic_score_gemma":0.003746848,"domain_scores_codex":[0.9996058,0.0001337773,0.00002461749,0.0001070947,0.0000735528,0.0000551008],"domain_scores_gemma":[0.9990155,0.0005609376,0.00006673485,0.000109123,0.0002167348,0.00003098594],"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":[0.0001115424,0.0001485298,0.001322442,0.00004053665,0.00002152374,0.00005686699,0.00002072802,0.9350131,0.002190035,0.001020747,0.0003821926,0.05967174],"study_design_scores_gemma":[0.000004606765,0.00003712608,0.0001562884,0.000001857464,0.000001181079,0.000004313094,0.000003551782,0.9980223,0.001403428,0.0002607595,0.0001030274,0.000001576959],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4207323,0.0004257628,0.5678782,0.000262772,0.00006950194,0.0002566857,0.0004115363,0.005123689,0.004839506],"genre_scores_gemma":[0.8149918,0.00009131496,0.1822505,0.00009364515,0.00001188168,0.000173636,0.0006486375,0.00007545682,0.001663224],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004846775,"threshold_uncertainty_score":0.009637117,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004792632690632809,"score_gpt":0.2520351778583594,"score_spread":0.2472425451677266,"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."}}