{"id":"W4311263210","doi":"10.37775/eis.2022.2.4","title":"Investigation of materials flow during the manufacturing process by experimental evidence and numerical approaches","year":2022,"lang":"en","type":"article","venue":"Mérnöki és Informatikai Megoldások","topic":"Metallurgy and Material Forming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Savaria (Canada)","funders":"","keywords":"Finite element method; Deformation (meteorology); Flow (mathematics); Process (computing); Mechanical engineering; Material flow; Computer science; Mechanics; Engineering; Structural engineering; Materials science; Physics; Composite material","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":[0.0003721786,0.0001807142,0.0002240545,0.00006386831,0.0002888569,0.00006822474,0.0002477846,0.00004241128,0.0003072555],"category_scores_gemma":[0.00001590387,0.0001468964,0.00003076908,0.00009570967,0.00006930056,0.000806787,0.0001858021,0.0001462997,0.000006310354],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006254199,"about_ca_system_score_gemma":0.00001336816,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001306513,"about_ca_topic_score_gemma":2.911008e-7,"domain_scores_codex":[0.9988431,0.00005127625,0.000458951,0.00011522,0.0002978139,0.0002335852],"domain_scores_gemma":[0.9995819,0.00004139931,0.000117495,0.0001891168,0.000009378175,0.0000606588],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000193774,0.00003675538,0.0002694624,0.0031473,0.0001655895,0.000005121675,0.04255865,0.07121971,0.8785844,0.000499248,0.0008305751,0.002489347],"study_design_scores_gemma":[0.000208123,0.0000379121,0.0003246144,0.00004635115,0.00001122378,0.00004462436,0.002368695,0.008517147,0.9877155,0.00005739473,0.0004837752,0.0001846422],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9981695,0.0004468953,0.0002526338,0.00002896906,0.0003206401,0.0002987878,0.00002951306,0.0001244723,0.0003286283],"genre_scores_gemma":[0.9993141,0.00001938671,0.0002908866,0.00003739146,0.00003994428,0.0001990899,0.00002862005,0.00002210306,0.00004852759],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.109131,"threshold_uncertainty_score":0.5990261,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02635778070245464,"score_gpt":0.2105055218017567,"score_spread":0.184147741099302,"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."}}