{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001213211,0.00102449,0.001130293,0.0009996841,0.000523761,0.00116649,0.00141854,0.0007915153,0.001024932],"category_scores_gemma":[0.002149354,0.0003857711,0.001014711,0.000777695,0.0002140712,0.001544361,0.001059209,0.001508685,0.0003780249],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005594419,"about_ca_system_score_gemma":0.0007417419,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009701519,"about_ca_topic_score_gemma":0.01045562,"domain_scores_codex":[0.9994876,0.00009835509,0.00003823453,0.0001843894,0.0001169699,0.00007429819],"domain_scores_gemma":[0.99928,0.0002666978,0.00006627595,0.00006017845,0.0002730125,0.00005385957],"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":[0.0002433391,0.0002696873,0.01647842,0.00008171215,0.0003551764,0.0002121005,0.0001574523,0.7388573,0.003201263,0.003209922,0.004105307,0.2328282],"study_design_scores_gemma":[0.000002223732,0.00001626395,0.0005395628,0.000003074277,0.00001899477,0.00001215213,0.000008723532,0.9983719,0.0002823565,0.0005069789,0.0002328776,0.00000485191],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1048683,0.00109122,0.8879898,0.0008234643,0.0002667542,0.00009990184,0.0004996021,0.001319909,0.003040961],"genre_scores_gemma":[0.9073479,0.0006762087,0.08613864,0.0003050655,0.0001965522,0.0001606006,0.001153402,0.00005218003,0.003969534],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009701519,"threshold_uncertainty_score":0.01929009,"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."}}