{"id":"W4410246225","doi":"10.1111/jfr3.70051","title":"Urban Flood Susceptibility Mapping for Toronto, Canada, Using Supervised Regression and Machine Learning Models","year":2025,"lang":"en","type":"article","venue":"Journal of Flood Risk Management","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Institute for Catastrophic Loss Reduction","keywords":"Flood myth; Regression analysis; Regression; Geography; Environmental science; Computer science; Machine learning; Statistics; Mathematics; Archaeology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001168488,0.0002545155,0.0003699823,0.0000980292,0.0004208369,0.00008135213,0.0003383328,0.00005336099,0.000141729],"category_scores_gemma":[0.0000302106,0.0002050227,0.0001311194,0.0001863482,0.00005073844,0.0005180234,0.0005009744,0.0002308411,7.068452e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00115917,"about_ca_system_score_gemma":0.00005920113,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1398742,"about_ca_topic_score_gemma":0.2295781,"domain_scores_codex":[0.9979439,0.0001439883,0.0006403118,0.0003617715,0.0005307677,0.0003792921],"domain_scores_gemma":[0.9990762,0.0000615848,0.000424941,0.0002689088,0.00003702721,0.0001313176],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007541546,0.0008530926,0.3648534,0.00116771,0.002141415,0.0001498578,0.001930961,0.2865069,0.002364944,0.002778474,0.0814256,0.2550735],"study_design_scores_gemma":[0.005557323,0.0004065185,0.05318768,0.0004957156,0.00116853,0.0000084384,0.004605839,0.852448,0.0003675793,0.003154027,0.077954,0.0006463217],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7603438,0.00527304,0.2158538,0.0006596049,0.001259794,0.001726973,0.00002239694,0.00004827109,0.01481231],"genre_scores_gemma":[0.9489056,0.002574686,0.0460852,0.0001827985,0.00008823904,0.0000138132,0.000006094965,0.00002394401,0.00211966],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5659412,"threshold_uncertainty_score":0.8658534,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01842073079285188,"score_gpt":0.2395971405982535,"score_spread":0.2211764098054017,"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."}}