{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004177535,0.00008443782,0.0001289403,0.0002615141,0.0001812112,0.000152469,0.001301099,0.00005770513,0.00006606578],"category_scores_gemma":[0.0009812783,0.00008205867,0.00005095992,0.00009932084,0.00006677908,0.001236266,0.0004546805,0.0002759534,0.000001405909],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008795972,"about_ca_system_score_gemma":0.00005589253,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002459123,"about_ca_topic_score_gemma":0.00001118416,"domain_scores_codex":[0.9984481,0.00007037623,0.0006496831,0.0001495307,0.0005710135,0.0001112562],"domain_scores_gemma":[0.9987139,0.000314187,0.0003592662,0.0001757,0.0003925549,0.00004435354],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001158957,0.0001332726,0.0006172608,0.00002718161,0.0001820022,0.00002200876,0.008426232,0.0001206673,0.004951973,0.1346396,0.01506926,0.8356946],"study_design_scores_gemma":[0.001613759,0.0003375183,0.0964497,0.0000545015,0.000146458,0.0009790732,0.01074764,0.05399936,0.006615353,0.7047368,0.1236822,0.0006376192],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1048548,0.0003106393,0.878392,0.006041536,0.009822049,0.00009349507,0.0004268725,0.00003093687,0.00002764071],"genre_scores_gemma":[0.9945484,0.00002942779,0.004456874,0.0001329259,0.0007016215,0.000005560479,0.0001190812,0.000003632472,0.000002463658],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8896936,"threshold_uncertainty_score":0.3346256,"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."}}