{"id":"W4293196010","doi":"10.2139/ssrn.4133587","title":"Prediction and Classification of Flood Susceptibility Across Canada","year":2022,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Flood myth; Geospatial analysis; Index (typography); Popularity; Computer science; Identification (biology); Geography; Data mining; Machine learning; Cartography; World Wide Web","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0004624537,0.0002996366,0.0002479938,0.001726869,0.001377851,0.001258334,0.000712916,0.0003571949,0.001442003],"category_scores_gemma":[0.002120584,0.0001641394,0.0003200037,0.002063954,0.0004283082,0.0003116608,0.000630448,0.0004253819,0.0002343864],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0138312,"about_ca_system_score_gemma":0.01404948,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9944993,"about_ca_topic_score_gemma":0.9961868,"domain_scores_codex":[0.9997043,0.0000344836,0.00001511257,0.00006213878,0.0000719047,0.0001120236],"domain_scores_gemma":[0.9984988,0.0002592103,0.0001504339,0.00004586456,0.0007867739,0.0002588777],"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":[0.0001796564,0.00006359786,0.9574463,0.00002087464,0.00009332604,0.0001045246,0.0006159501,0.01348835,0.001227404,0.0006434579,0.002928084,0.02318847],"study_design_scores_gemma":[0.000009359672,0.00001438326,0.964096,0.00001833377,0.00002110615,0.00002562398,0.001306472,0.032352,0.0002797293,0.0002615366,0.001599395,0.00001608016],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9958342,0.0001091778,0.0004800119,0.0001911047,0.000004190639,0.00001399898,0.00157017,0.00003715656,0.001760071],"genre_scores_gemma":[0.9971015,0.00007408428,0.0003614928,0.00001422457,0.00000143083,0.000004760476,0.001158946,0.00000747421,0.001276151],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0138312,"threshold_uncertainty_score":0.1003528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007492271543941473,"score_gpt":0.2221142694997358,"score_spread":0.2146219979557943,"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."}}