{"id":"W4378978290","doi":"10.1016/j.scs.2023.104661","title":"Generative design for COVID-19 and future pathogens using stochastic multi-agent simulation","year":2023,"lang":"en","type":"article","venue":"Sustainable Cities and Society","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Autodesk (Canada)","funders":"","keywords":"SAFER; Generative Design; Computer science; Workflow; Set (abstract data type); Transmission (telecommunications); Generative grammar; Machine learning; Artificial intelligence; Simulation; Mathematical optimization; Engineering; Mathematics; Computer security; Programming language; Operations management","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":[0.001868942,0.0006852716,0.001274191,0.001033061,0.001162465,0.001989407,0.002115631,0.002119017,0.01046395],"category_scores_gemma":[0.00694395,0.001134039,0.001702926,0.0005748944,0.001841402,0.001351929,0.002882808,0.001644025,0.00100004],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001970578,"about_ca_system_score_gemma":0.001900651,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009758399,"about_ca_topic_score_gemma":0.01008472,"domain_scores_codex":[0.9992034,0.0003147629,0.00002953453,0.0001560886,0.0001626704,0.0001335838],"domain_scores_gemma":[0.9962321,0.002612172,0.000237079,0.0002701088,0.0004076714,0.0002408424],"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":[0.00001264788,0.00001372362,0.0003096293,0.00001098494,0.000009269521,0.00003177824,0.00003640035,0.9795065,0.0001988941,0.01799397,0.0002175543,0.001658652],"study_design_scores_gemma":[0.000003914811,0.00000471675,0.00002442656,0.000002344618,0.000002858075,0.000005757079,0.000006067214,0.9950517,0.00005163303,0.004608684,0.0002352614,0.000002645287],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04656717,0.0001584767,0.9383369,0.0006835542,0.0001256671,0.000112109,0.0001888909,0.0005221926,0.013305],"genre_scores_gemma":[0.8597277,0.0001798448,0.1230056,0.0002705698,0.0000588844,0.0003412485,0.0002933449,0.0003376892,0.01578512],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01046395,"threshold_uncertainty_score":0.03500539,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03589054838236782,"score_gpt":0.2719120963032117,"score_spread":0.2360215479208438,"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."}}