{"id":"W3199541213","doi":"10.1117/12.2599630","title":"Optical remote sensing for urban flood applications: Canadian case studies","year":2021,"lang":"en","type":"article","venue":"","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Flood myth; Remote sensing; Urbanization; Flash flood; Geospatial analysis; Geography; Environmental science; Geographic information system; Pluvial; Natural disaster; Urban planning; Emergency management; Meteorology; Geology; Civil engineering","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":[0.0008111696,0.0008004283,0.0003103701,0.001243039,0.002695301,0.001301678,0.001146851,0.0007575123,0.00187783],"category_scores_gemma":[0.001622206,0.0002504242,0.0005555403,0.004905259,0.001108135,0.0004508417,0.0008088058,0.0005534724,0.0001455781],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02412769,"about_ca_system_score_gemma":0.01386435,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9868523,"about_ca_topic_score_gemma":0.9923716,"domain_scores_codex":[0.9992738,0.0001232936,0.00002271603,0.00005771847,0.0002799297,0.0002424538],"domain_scores_gemma":[0.9992281,0.0002220733,0.00004997205,0.00005010958,0.0003591049,0.00009057186],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.001065645,0.001591499,0.1699654,0.001223603,0.0003223574,0.01209002,0.006427358,0.5595221,0.008353168,0.02354274,0.04410668,0.1717894],"study_design_scores_gemma":[0.0004592476,0.0005560103,0.3338646,0.0002950781,0.0004438239,0.002130058,0.02872929,0.5115688,0.009455737,0.005526147,0.1064398,0.0005314496],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9566944,0.00102304,0.003911212,0.001443945,0.00003253945,0.0004729236,0.00429024,0.0001504864,0.03198126],"genre_scores_gemma":[0.980413,0.001966377,0.008522317,0.0001282543,0.00001188033,0.00009794848,0.001755191,0.00003501789,0.007069984],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02412769,"threshold_uncertainty_score":0.1750595,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02196428407793576,"score_gpt":0.2896475034218078,"score_spread":0.2676832193438721,"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."}}