{"id":"W4407145232","doi":"10.1080/03081079.2025.2459204","title":"A bi-objective location routing optimization with fuzzy time-dependent societal risks for enhancing urban medical waste management system","year":2025,"lang":"en","type":"article","venue":"International Journal of General Systems","topic":"Healthcare and Environmental Waste Management","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Fuzzy logic; Routing (electronic design automation); Computer science; Risk analysis (engineering); Operations research; Business; Engineering; Artificial intelligence; Computer network","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001016103,0.0001590279,0.0003341015,0.000273238,0.00009235504,0.00007295188,0.0002135549,0.00009265524,0.00001058732],"category_scores_gemma":[0.00003836872,0.0001245548,0.0001212088,0.0001483638,0.00002834515,0.0001339132,0.0000779067,0.0001666239,0.000007529386],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001339481,"about_ca_system_score_gemma":0.0001807192,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001526483,"about_ca_topic_score_gemma":0.00001408439,"domain_scores_codex":[0.9975363,0.00009025336,0.0008102166,0.0002178693,0.001141091,0.0002042838],"domain_scores_gemma":[0.9987802,0.00004334926,0.0004762468,0.0001230403,0.0004412125,0.0001359865],"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":[0.002148454,0.0005532664,0.00634714,0.002434453,0.006127759,0.0006197976,0.001100483,0.9455501,0.001166004,0.005989786,0.005850963,0.02211178],"study_design_scores_gemma":[0.01314197,0.001643984,0.002213721,0.01407523,0.001150375,0.001437798,0.02237182,0.9379782,0.002099018,0.00003443675,0.003321667,0.0005317755],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05514167,0.0005524079,0.9298723,0.002418884,0.002556108,0.001339438,0.00001178826,0.00004137469,0.008065985],"genre_scores_gemma":[0.9916207,0.00005419091,0.003236673,0.0002804146,0.001173061,0.00005324103,0.00003363961,0.00002208972,0.003525969],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.936479,"threshold_uncertainty_score":0.5079196,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01314699085717446,"score_gpt":0.2967086107272248,"score_spread":0.2835616198700503,"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."}}