{"id":"W4292230725","doi":"10.1109/compsac54236.2022.00198","title":"Predictive Analytics for Supporting Environmental Sustainability and Disaster Management","year":2022,"lang":"en","type":"article","venue":"2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)","topic":"Data Stream Mining Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Sustainability; Analytics; Predictive analytics; Computer science; Environmental data; Big data; Data analysis; Risk analysis (engineering); Emergency management; Data science; Environmental resource management; Business; Environmental science; Data mining; Political science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.002324906,0.001223619,0.0007843247,0.002475174,0.0006659193,0.00215944,0.001780698,0.001047782,0.001394106],"category_scores_gemma":[0.008574405,0.0004025691,0.000778478,0.003739784,0.0006752837,0.003293513,0.001761848,0.002022445,0.0005728528],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009280187,"about_ca_system_score_gemma":0.001572554,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007538085,"about_ca_topic_score_gemma":0.007300359,"domain_scores_codex":[0.9986413,0.0002906465,0.0001084686,0.0002404581,0.0006274044,0.00009180683],"domain_scores_gemma":[0.9956416,0.002426984,0.0003532704,0.0006005345,0.0008182838,0.0001594121],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004291563,0.0004003168,0.01793443,0.0009395634,0.0003700395,0.0007151265,0.0007118882,0.3134295,0.01089597,0.05589737,0.04600705,0.5522696],"study_design_scores_gemma":[0.0000267175,0.00005598484,0.00208594,0.0001162752,0.00006380914,0.0001253786,0.0002959374,0.8786965,0.005430423,0.09363575,0.01943017,0.00003708653],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03311704,0.003411771,0.9372852,0.005519336,0.0003231899,0.0003090445,0.003577009,0.008570907,0.007886495],"genre_scores_gemma":[0.6690343,0.005115891,0.3156261,0.0008445154,0.0004357491,0.0002906351,0.006475927,0.0003040354,0.001872755],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007538085,"threshold_uncertainty_score":0.01498842,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01106206126923,"score_gpt":0.251044855989244,"score_spread":0.239982794720014,"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."}}