{"id":"W2622345998","doi":"10.5539/emr.v6n2p1","title":"An Expert System for Local Flood Response Coordination and Training","year":2017,"lang":"en","type":"article","venue":"Engineering Management Research","topic":"Disaster Management and Resilience","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Flood myth; Construct (python library); Scalability; Component (thermodynamics); Emergency response; Computer science; Emergency management; Risk analysis (engineering); Disaster response; Training (meteorology); Knowledge management; Computer security; Business; Process management; Engineering; Political science; Computer network; Geography; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.004659248,0.00007867419,0.00009117964,0.0002405609,0.001468654,0.0007328557,0.0005868713,0.00004240707,0.000007449782],"category_scores_gemma":[0.0002076652,0.00007938845,0.00002308955,0.0001114831,0.0002214877,0.000482888,0.0001594189,0.00008636443,0.000007043359],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001145832,"about_ca_system_score_gemma":0.00001931005,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001510573,"about_ca_topic_score_gemma":0.00004758792,"domain_scores_codex":[0.9985175,0.0001368408,0.0001032048,0.0002718487,0.0005111955,0.0004594047],"domain_scores_gemma":[0.9992867,0.0001419972,0.00002730608,0.0003515424,0.00006943212,0.0001230593],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004038358,0.0001034068,0.0003389208,0.0006799616,0.0001117493,0.00007710762,0.03175065,0.002482276,0.001538156,0.6083534,0.003271015,0.3508896],"study_design_scores_gemma":[0.002492763,0.0005175365,0.03697204,0.000632329,0.00003559105,0.000001418053,0.238891,0.2074614,0.0003842553,0.0006741098,0.5111856,0.000751966],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6004347,0.0003165893,0.2745992,0.007148265,0.001446662,0.003885237,0.000005900477,0.0006823994,0.1114811],"genre_scores_gemma":[0.9924834,0.00002989313,0.001484958,0.000005988019,0.0001650853,0.0001593323,0.000001557919,0.00001360328,0.005656154],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6076792,"threshold_uncertainty_score":0.9998313,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08097553757235768,"score_gpt":0.4114371040125053,"score_spread":0.3304615664401477,"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."}}