{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004646408,0.0002747091,0.0002889939,0.0001776084,0.0008277731,0.0002427128,0.001054487,0.00004650753,0.00001616702],"category_scores_gemma":[0.00001460961,0.0003072632,0.00006715259,0.0002816592,0.0002370317,0.000473713,0.001728374,0.000237683,0.000001551553],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001739726,"about_ca_system_score_gemma":0.00007220038,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000243442,"about_ca_topic_score_gemma":0.000002782318,"domain_scores_codex":[0.997712,0.00009582097,0.0004130678,0.0009973878,0.0003292581,0.0004524671],"domain_scores_gemma":[0.9984452,0.0002571891,0.0002264864,0.0008081084,0.00007819385,0.0001848117],"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":[0.00007139669,0.0007325496,0.01249256,0.0003523988,0.0002003048,0.00002144238,0.005122432,0.0006863371,0.00002846509,0.08683968,0.01050974,0.8829427],"study_design_scores_gemma":[0.003151353,0.002704782,0.03693875,0.00007140671,0.0003309034,0.0001690816,0.01903583,0.5198562,0.0002532482,0.1875513,0.2272151,0.002722113],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01275025,0.0000641986,0.9834753,0.0003535834,0.0000995307,0.001603834,0.001199593,0.0003700673,0.00008363745],"genre_scores_gemma":[0.8172745,0.00004965032,0.1799663,0.0003428257,0.00006784471,0.001758467,0.0003013538,0.00002220351,0.000216892],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8802206,"threshold_uncertainty_score":0.999938,"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."}}