{"id":"W4283818380","doi":"10.3390/w14132120","title":"Data-Driven Community Flood Resilience Prediction","year":2022,"lang":"en","type":"article","venue":"Water","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Sciences and Engineering Research Council of Canada; McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Flood myth; Community resilience; Resilience (materials science); Environmental resource management; Vulnerability (computing); Climate change; Flood mitigation; Categorization; Computer science; Environmental planning; Environmental science; Risk analysis (engineering); Geography; Business; Computer security; Artificial intelligence; Ecology","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":[0.0009853016,0.0004198534,0.0003423895,0.001001284,0.0002811189,0.0004537853,0.0008572834,0.0004786407,0.0008252159],"category_scores_gemma":[0.003024725,0.0001688492,0.0003622945,0.0005983585,0.0002954291,0.0008987478,0.001039455,0.0005919972,0.0001595942],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008835096,"about_ca_system_score_gemma":0.0007820052,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01298831,"about_ca_topic_score_gemma":0.01308989,"domain_scores_codex":[0.9998046,0.0000510176,0.000009199405,0.00005999258,0.00003546783,0.00003980445],"domain_scores_gemma":[0.9989416,0.0004875296,0.0001537386,0.00008187414,0.0002267469,0.0001085198],"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":[0.0000567731,0.00008684011,0.02184701,0.00001754937,0.00002273191,0.00006684825,0.00005784715,0.9545484,0.0006547067,0.0009646637,0.0006246983,0.02105203],"study_design_scores_gemma":[9.438657e-7,0.000004726193,0.0007377867,8.659961e-7,8.489968e-7,0.000002194702,0.00001054791,0.9986377,0.0001382675,0.0004270316,0.00003754292,0.000001489422],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7566314,0.00009690104,0.2386345,0.0005627986,0.00002779247,0.0001129786,0.001141131,0.0008984572,0.001893995],"genre_scores_gemma":[0.9817712,0.00002051122,0.01745619,0.0000212807,0.000006289662,0.00004317719,0.0003890808,0.000008084832,0.0002841542],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01298831,"threshold_uncertainty_score":0.02582538,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02576564714327579,"score_gpt":0.2471501535141425,"score_spread":0.2213845063708667,"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."}}