{"id":"W1515386252","doi":"10.1023/a:1022921823614","title":"Flooding in the Red River Basin – Lessons from Post Flood Activities","year":2003,"lang":"en","type":"article","venue":"Natural Hazards","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":34,"is_retracted":false,"has_abstract":false,"ca_institutions":"KGS Group (Canada); Western University","funders":"U.S. Army Corps of Engineers","keywords":"Flood myth; Preparedness; Natural hazard; Environmental planning; Standardization; Commission; Floodplain; Emergency management; Population; Environmental resource management; Business; Geography; Computer science; Environmental science; Political science; Cartography","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0007543956,0.0001361808,0.0001069845,0.0003959674,0.0005902112,0.0009455546,0.0004457042,0.0004626313,0.0009030837],"category_scores_gemma":[0.001641848,0.0001304645,0.0001740045,0.0008022476,0.0007290922,0.001029892,0.0005515011,0.000402453,0.00008146439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001472986,"about_ca_system_score_gemma":0.001040113,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1129211,"about_ca_topic_score_gemma":0.1854086,"domain_scores_codex":[0.999797,0.00009834426,0.000007739994,0.00002046172,0.00002885314,0.00004760646],"domain_scores_gemma":[0.9993656,0.000250526,0.00009883924,0.0000604051,0.0001264904,0.00009805062],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005380997,0.0005364266,0.5725318,0.0002747926,0.0001960316,0.004031726,0.04551443,0.006717541,0.001199645,0.01433208,0.01120474,0.3429228],"study_design_scores_gemma":[0.00001331697,0.0001712354,0.9536923,0.00008835069,0.00003903276,0.0003060912,0.0213831,0.001123397,0.0002386804,0.00324009,0.01968198,0.00002241482],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9863732,0.001383047,0.0002418259,0.002995373,0.00002606967,0.000007798757,0.0001176201,0.000005668302,0.008849281],"genre_scores_gemma":[0.9949768,0.002294488,0.0001754605,0.0001625004,0.00003428194,0.000004427914,0.00008711907,0.00000349959,0.002261292],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1129211,"threshold_uncertainty_score":0.2245278,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009249528471916109,"score_gpt":0.2519258521454898,"score_spread":0.2426763236735738,"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."}}