{"id":"W4223943788","doi":"10.3390/bdcc6020042","title":"An Emergency Event Detection Ensemble Model Based on Big Data","year":2022,"lang":"en","type":"article","venue":"Big Data and Cognitive Computing","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Big data; Event (particle physics); Computer science; Social media; Data science; Data mining; World Wide Web","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"design_other","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003445274,0.0001176405,0.00008930108,0.0001248922,0.0002739084,0.00002975939,0.0004008,0.00002223631,0.00000725852],"category_scores_gemma":[0.00002307305,0.0001361456,0.00001236399,0.0001563236,0.00001079045,0.000139344,0.0006350868,0.0001679444,0.000002247116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001950521,"about_ca_system_score_gemma":0.00001151239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001153056,"about_ca_topic_score_gemma":0.00002706279,"domain_scores_codex":[0.9990664,0.00005271898,0.000161862,0.0003907027,0.0001691421,0.0001591564],"domain_scores_gemma":[0.9993114,0.00002688956,0.00003314486,0.0005555269,0.00002024171,0.00005281332],"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":[0.00001346596,0.00005511254,0.00004596338,0.0000217405,0.00001868687,0.000003219086,0.00004374855,0.01630811,0.0005250256,0.00001450567,0.00274139,0.9802091],"study_design_scores_gemma":[0.0002596409,0.00008198863,0.0005454358,0.00002276757,0.00003824732,0.000002185878,0.0001974422,0.9955199,0.0002611834,0.00002667435,0.002890268,0.0001543106],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02046119,0.00009328123,0.9755867,0.00001663253,0.0006973151,0.000179286,0.0006661633,0.001228424,0.001071013],"genre_scores_gemma":[0.9973422,0.00004947787,0.0004789602,0.0001312402,0.0001847205,0.00001196945,0.001771597,0.00002175872,0.000008075142],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9800547,"threshold_uncertainty_score":0.5551858,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08696110024705422,"score_gpt":0.2883428574207259,"score_spread":0.2013817571736717,"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."}}