{"id":"W4210424919","doi":"10.3390/su14031655","title":"Sustainability Analysis and Environmental Decision-Making Using Simulation, Optimization, and Computational Analytics","year":2022,"lang":"en","type":"article","venue":"Sustainability","topic":"Sustainable Industrial Ecology","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Liikesivistysrahasto","keywords":"Sustainability; Analytics; Computer science; Management science; Data science; Big data; Decision support system; Computational intelligence; Engineering; Artificial intelligence; Data mining","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009071019,0.0002294775,0.0003784282,0.0005289147,0.0006947501,0.00007579372,0.0001353922,0.0001064624,0.0003658548],"category_scores_gemma":[0.001425838,0.0002850583,0.00008862116,0.001294818,0.0002619681,0.0002533065,0.0003909678,0.000309272,1.106177e-7],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006100751,"about_ca_system_score_gemma":0.0003917209,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006078999,"about_ca_topic_score_gemma":0.00001270576,"domain_scores_codex":[0.9979838,0.0002420934,0.0005232962,0.0004946051,0.0003271725,0.000429067],"domain_scores_gemma":[0.9979807,0.001129554,0.00009495395,0.000337225,0.0003371594,0.0001204144],"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":[0.00004148715,0.0000510777,0.1208688,0.00006214204,0.0001560763,0.00001898675,0.0003898155,0.874706,2.861509e-7,0.0001572225,0.000009309204,0.003538848],"study_design_scores_gemma":[0.0003522706,0.00004239399,0.04083118,0.000001078187,0.0002482033,0.00001002999,0.007113196,0.927529,4.562471e-7,0.02345319,0.0001777053,0.0002412284],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.64008,0.0001084075,0.3590292,0.0001152351,0.0000563434,0.0004741343,0.00003852511,0.00008877448,0.000009389641],"genre_scores_gemma":[0.9950124,0.000003165594,0.004792262,0.00003262345,0.00003025096,0.00003566462,0.00005248498,0.00002839796,0.00001274223],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3549325,"threshold_uncertainty_score":0.9999602,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007603801504567063,"score_gpt":0.2603657921416661,"score_spread":0.252761990637099,"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."}}