{"id":"W4387456041","doi":"10.22541/au.169682631.16687135/v1","title":"A random forest machine learning model to detect fluvial hazards","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Hydrology and Sediment Transport Processes","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Rimouski; Concordia University","funders":"","keywords":"Fluvial; Sinuosity; Flooding (psychology); Random forest; Hydrology (agriculture); Environmental science; Scale (ratio); Hazard; Computer science; Geology; Cartography; Artificial intelligence; Geography; Geomorphology; Geotechnical 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":[0.001872965,0.000800709,0.0007148056,0.0008582269,0.0004023339,0.0006150869,0.001210467,0.001040459,0.001553193],"category_scores_gemma":[0.003007322,0.0003008205,0.0007252679,0.0006021323,0.0003464308,0.000659094,0.0003643256,0.0009684298,0.0005902523],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000804979,"about_ca_system_score_gemma":0.0009597245,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0290925,"about_ca_topic_score_gemma":0.02181844,"domain_scores_codex":[0.9996258,0.0001313036,0.00002275441,0.0001116546,0.00005027986,0.00005818355],"domain_scores_gemma":[0.9989223,0.0006705838,0.00008368464,0.0000397735,0.0002493834,0.0000342584],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001017193,0.00009045681,0.004389002,0.00003340145,0.0000549658,0.00007127757,0.00002871669,0.9348359,0.0006183809,0.001520026,0.001793432,0.05646271],"study_design_scores_gemma":[0.000003510601,0.000011094,0.0001789096,0.000002413921,0.00000326559,0.000005151275,0.000001944126,0.9991741,0.0000589145,0.0004677697,0.00009078073,0.000002103017],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1365315,0.000630812,0.8560766,0.0007107951,0.0001313102,0.0001744021,0.0009036633,0.001923186,0.002917724],"genre_scores_gemma":[0.862619,0.0002378746,0.131045,0.0002490043,0.0001076017,0.0003226241,0.001329766,0.00006910151,0.004019991],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0290925,"threshold_uncertainty_score":0.05784631,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02104644587764578,"score_gpt":0.2497973557376093,"score_spread":0.2287509098599635,"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."}}