{"id":"W4313455359","doi":"10.1504/ijcis.2023.10046166","title":"Intelligent Agent for Hurricane Emergency Identification and Text Information Extraction from Streaming Social Media Big Data","year":2022,"lang":"en","type":"article","venue":"International Journal of Critical Infrastructures","topic":"Technology and Security Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Institute for Catastrophic Loss Reduction; National Aeronautics and Space Administration; Qatar Foundation; U.S. Department of Homeland Security; Qatar University; Northrop Grumman; Old Dominion University; National Science Foundation","keywords":"Social media; Extraction (chemistry); Identification (biology); Computer science; Emergency rooms; Medical emergency; Data science; Computer security; World Wide Web; Medicine; Chemistry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000790384,0.000687308,0.0005908143,0.001655746,0.000644187,0.001305053,0.0006866571,0.0006740829,0.001989311],"category_scores_gemma":[0.002551378,0.0002862047,0.0006098903,0.0008351735,0.0002730159,0.001506108,0.0007360399,0.0008485617,0.00120649],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004030815,"about_ca_system_score_gemma":0.0008318805,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002842446,"about_ca_topic_score_gemma":0.005025886,"domain_scores_codex":[0.9994592,0.000117597,0.00006201628,0.0001494247,0.0001766669,0.00003500878],"domain_scores_gemma":[0.9988231,0.0005605232,0.0001510909,0.0001508985,0.0002410325,0.0000733539],"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":[0.001345775,0.001845427,0.02211676,0.001650127,0.0005678373,0.002086493,0.00154217,0.05672662,0.08217417,0.01779383,0.05202537,0.7601254],"study_design_scores_gemma":[0.00007685539,0.0002010701,0.004492996,0.00006168617,0.000120929,0.0003409128,0.0004711844,0.920504,0.02856828,0.008361867,0.03673045,0.00006983377],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07222099,0.0008208136,0.8923152,0.002378718,0.0003889268,0.001661103,0.004301654,0.01713551,0.008776939],"genre_scores_gemma":[0.3592902,0.0005060563,0.625963,0.0006106815,0.0001663185,0.000893648,0.005090739,0.0002007129,0.007278719],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002842446,"threshold_uncertainty_score":0.006654859,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04317393133065672,"score_gpt":0.336123042595238,"score_spread":0.2929491112645812,"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."}}