{"id":"W4385981250","doi":"10.1051/e3sconf/202341505029","title":"Advancing debris flow hazard and risk assessments using debris flow modeling and radar derived rainfall intensity data","year":2023,"lang":"en","type":"article","venue":"E3S Web of Conferences","topic":"Landslides and related hazards","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Stantec (Canada)","funders":"","keywords":"Debris flow; Debris; Environmental science; Watershed; Hazard; Hazard analysis; Flow (mathematics); Hydrology (agriculture); Computer science; Geology; Meteorology; Engineering; Geography; Geotechnical engineering; Reliability engineering; Mathematics","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":[0.002653968,0.0008983415,0.0006694162,0.002439612,0.0002300608,0.001645869,0.0008866612,0.0006352115,0.001506829],"category_scores_gemma":[0.005570968,0.0004317902,0.0005981911,0.001204035,0.000320171,0.002956123,0.0007602771,0.0007809644,0.0005227577],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008139217,"about_ca_system_score_gemma":0.0006730625,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004475819,"about_ca_topic_score_gemma":0.006841192,"domain_scores_codex":[0.9991835,0.0003169253,0.00005202981,0.00009598775,0.0003031541,0.00004845744],"domain_scores_gemma":[0.9961622,0.001886954,0.0008536522,0.0003996179,0.0005694106,0.0001281779],"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":[0.0000853476,0.0003208609,0.1356231,0.0002203913,0.000180854,0.0001436863,0.0001444536,0.6841772,0.00779784,0.005756678,0.001881474,0.1636681],"study_design_scores_gemma":[0.00001617923,0.0001465788,0.03795536,0.00009634549,0.00004954902,0.0001187685,0.0001559633,0.9446526,0.004305976,0.008888523,0.00353525,0.00007885831],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4224761,0.001452724,0.5564021,0.001502623,0.0001110927,0.0003003954,0.002374831,0.001996209,0.01338391],"genre_scores_gemma":[0.8451921,0.001055005,0.151032,0.00009678914,0.00009037835,0.0000948435,0.00120447,0.0000804466,0.001154012],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004475819,"threshold_uncertainty_score":0.0140357,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03633466477784131,"score_gpt":0.2831389353806237,"score_spread":0.2468042706027824,"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."}}