{"id":"W2035634293","doi":"10.1016/j.procs.2013.06.116","title":"Freight Market Interactions Simulation (FREMIS): An Agent-based Modeling Framework","year":2013,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Urban and Freight Transport Logistics","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Scope (computer science); Focus (optics); Conceptual framework; Rationality; Agent-based model; Product (mathematics); Presentation (obstetrics); Conceptual model; Operations research; Artificial intelligence; Database","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":[0.0001327539,0.0001488799,0.0001084767,0.0001565518,0.0001561637,0.0002142918,0.0004018533,0.00005295736,0.0001948546],"category_scores_gemma":[0.00002374182,0.0001431472,0.00003332095,0.0004685207,0.0001005684,0.0008558505,0.00002256554,0.0002104211,0.00006760827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007039687,"about_ca_system_score_gemma":0.000051332,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001189875,"about_ca_topic_score_gemma":0.000004658876,"domain_scores_codex":[0.9988926,0.000007116274,0.0002126279,0.0002983455,0.0002676205,0.0003216707],"domain_scores_gemma":[0.9992508,0.00007291303,0.00002291977,0.0002890135,0.0001705696,0.0001937261],"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":[0.000001307672,0.00002550527,0.0006115217,0.00002526652,0.000003023987,0.000001397177,0.0001873793,0.9927074,0.0002875568,0.0003182308,0.0001957711,0.005635688],"study_design_scores_gemma":[0.0000720708,0.00001995159,0.001213857,0.00003542368,0.000005824497,0.000001380019,0.000005004184,0.9964918,0.0002522941,0.001469637,0.0002449786,0.0001877737],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05306917,0.00003213001,0.9441167,0.00002947737,0.0009755304,0.0001641632,0.000002885041,0.0004276453,0.00118236],"genre_scores_gemma":[0.8289731,0.000002930717,0.1705998,0.0001356189,0.0002384023,0.00001720224,0.000005511917,0.00001571906,0.00001170873],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.775904,"threshold_uncertainty_score":0.5837374,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0314160260199044,"score_gpt":0.2370781041559504,"score_spread":0.205662078136046,"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."}}