{"id":"W4286462504","doi":"10.3390/w14142280","title":"A Comprehensive Approach for Floodplain Mapping through Identification of Hazard Using Publicly Available Data Sets over Canada","year":2022,"lang":"en","type":"article","venue":"Water","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Institute for Catastrophic Loss Reduction","keywords":"Flood myth; Floodplain; Flooding (psychology); Return period; Hazard; Population; Geography; 100-year flood; Identification (biology); Environmental science; Hydrology (agriculture); Environmental resource management; Cartography; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001009232,0.0005813443,0.0003986527,0.005436407,0.001977708,0.002193428,0.001267033,0.0003311006,0.00207617],"category_scores_gemma":[0.004841181,0.0003449077,0.0005997536,0.01071177,0.0003504827,0.0006718987,0.001637551,0.0005401612,0.0003832173],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02733947,"about_ca_system_score_gemma":0.05611794,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9947414,"about_ca_topic_score_gemma":0.9947932,"domain_scores_codex":[0.9988458,0.0001060709,0.00006878748,0.0001647317,0.0005562656,0.0002582574],"domain_scores_gemma":[0.9961407,0.0003211042,0.0002335996,0.0003341956,0.002714123,0.0002563728],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002385464,0.0003338536,0.5219945,0.000635836,0.0004298244,0.0008385123,0.003734264,0.07167459,0.004797671,0.01562705,0.09082048,0.2888748],"study_design_scores_gemma":[0.00004384906,0.00004161107,0.7525076,0.0003421304,0.0001523503,0.0001814509,0.007732819,0.111669,0.005028507,0.002495023,0.119574,0.0002315447],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5526168,0.001077055,0.03968756,0.002513645,0.00006501264,0.001192398,0.3514055,0.003409388,0.04803265],"genre_scores_gemma":[0.8134358,0.001148009,0.05515312,0.0002231995,0.0000164141,0.0005239518,0.1207003,0.0002186829,0.008580535],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02733947,"threshold_uncertainty_score":0.1983627,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07278714240963191,"score_gpt":0.2684699771841805,"score_spread":0.1956828347745486,"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."}}