{"id":"W3212360012","doi":"10.32920/ryerson.14662929.v1","title":"Design and Development of an Intelligent Agent-Based Supply Chain Simulation System","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Supply chain; Point of sale; Supply chain management; Computer science; Product (mathematics); Loan; Production (economics); Order (exchange); Business; Finance; Marketing; Economics; Microeconomics","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":[0.0007462601,0.0005867028,0.0008455697,0.0004701416,0.0005659132,0.001225425,0.001681613,0.0008281987,0.004137615],"category_scores_gemma":[0.001297335,0.0004794083,0.0005180819,0.0004157428,0.0003187113,0.001103205,0.000781289,0.0008889989,0.001133608],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007786189,"about_ca_system_score_gemma":0.001516669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003651338,"about_ca_topic_score_gemma":0.0019277,"domain_scores_codex":[0.9996191,0.0001090163,0.00004926586,0.00007775928,0.0001133375,0.0000315398],"domain_scores_gemma":[0.999499,0.0001623204,0.00004620743,0.00006302221,0.0001664782,0.0000630551],"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":[0.0004178183,0.0004909885,0.004143141,0.0004877012,0.0001659255,0.0004945582,0.0004817868,0.7796133,0.03098177,0.02832947,0.005924212,0.1484694],"study_design_scores_gemma":[0.00004011134,0.00003606058,0.000134985,0.00001007521,0.00002004937,0.00002793514,0.00001550952,0.9900537,0.003183041,0.0009431272,0.005524398,0.00001100587],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01512966,0.00008582355,0.971034,0.0001429456,0.00004801642,0.000512879,0.0001531283,0.008311256,0.00458214],"genre_scores_gemma":[0.2724989,0.0002967971,0.7203413,0.00011873,0.00002388615,0.001166954,0.0007318018,0.0002600989,0.004561736],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004137615,"threshold_uncertainty_score":0.01384169,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04549506273547051,"score_gpt":0.2627886666079072,"score_spread":0.2172936038724367,"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."}}