{"id":"W4200584305","doi":"10.18280/acsm.450502","title":"Identification Material Distribution Process to Improve Material Handling Performance Using Risk Matrix Analysis (Case Study at Paper Manufacturing)","year":2021,"lang":"en","type":"article","venue":"Annales de Chimie Science des Matériaux","topic":"Management and Optimization Techniques","field":"Business, Management and Accounting","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Material handling; Pallet; Material flow; Value stream mapping; Process (computing); Lean manufacturing; Schedule; Product (mathematics); Manufacturing engineering; Root cause; Materials management; Identification (biology); Process engineering; Computer science; Operations management; Engineering; Mechanical engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001863844,0.0008788133,0.0004229766,0.002011655,0.0007947444,0.002036483,0.0006337373,0.0008324673,0.005129094],"category_scores_gemma":[0.002981673,0.0002981736,0.0008215577,0.001474278,0.000321413,0.001283134,0.0005934425,0.0005340964,0.0005761261],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001327034,"about_ca_system_score_gemma":0.001442335,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005948914,"about_ca_topic_score_gemma":0.004570655,"domain_scores_codex":[0.9988719,0.0003633123,0.00006931728,0.000139963,0.0004233797,0.0001321595],"domain_scores_gemma":[0.9982009,0.0008798986,0.000216338,0.0001451532,0.0004724269,0.00008532251],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.000879253,0.001561215,0.02743259,0.0007147162,0.0001076413,0.003009119,0.002102736,0.5291792,0.05742008,0.01427461,0.002795183,0.3605236],"study_design_scores_gemma":[0.00008937586,0.001005155,0.01481183,0.00007122438,0.0001090118,0.0006007761,0.002473636,0.9220234,0.04432403,0.006127454,0.00826849,0.00009567357],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5683629,0.0003104501,0.4116491,0.0005443089,0.00004476413,0.001012341,0.0003735602,0.001227451,0.01647521],"genre_scores_gemma":[0.8147083,0.0001784629,0.1792489,0.0000261183,0.000009251678,0.0001650172,0.0002197424,0.00005716367,0.005387093],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005948914,"threshold_uncertainty_score":0.01715857,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02002079794367765,"score_gpt":0.2868575242081274,"score_spread":0.2668367262644497,"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."}}