{"id":"W2358017220","doi":"","title":"Activity analysis of debris flow using genetic neural network","year":2003,"lang":"en","type":"article","venue":"","topic":"Landslides and related hazards","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"CAE (Canada)","funders":"","keywords":"Debris flow; Artificial neural network; Genetic algorithm; Computer science; Debris; Set (abstract data type); Fuzzy logic; Data mining; Artificial intelligence; Geography; Machine learning; Meteorology","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":[0.0004856849,0.0004662555,0.0003988101,0.0008672808,0.0002449373,0.0005957786,0.0004811129,0.0005803824,0.0006233197],"category_scores_gemma":[0.001599456,0.000216806,0.0004363727,0.0006965529,0.0002693487,0.0006071569,0.0002233574,0.0003424973,0.0001055281],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009846115,"about_ca_system_score_gemma":0.0006424944,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0171863,"about_ca_topic_score_gemma":0.00970965,"domain_scores_codex":[0.9998042,0.00005683341,0.00001069538,0.00004778583,0.00004922393,0.00003124262],"domain_scores_gemma":[0.9996814,0.0001596441,0.00004375079,0.00001577182,0.00008639514,0.00001305641],"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":[0.00004460497,0.00003107867,0.002774573,0.00001599663,0.00003328947,0.00003424042,0.00002655751,0.9670163,0.001067529,0.0008390748,0.0001210315,0.02799581],"study_design_scores_gemma":[0.0000021394,0.0000076008,0.0004541813,0.000001219311,0.000003254027,0.000003273119,0.000002918992,0.9988403,0.0002553446,0.0003891669,0.00003863507,0.000002031983],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.395821,0.0002582334,0.5987654,0.0002642665,0.00003521934,0.00007803981,0.0001556629,0.0007902462,0.003831906],"genre_scores_gemma":[0.9631985,0.0000978387,0.03535441,0.00001822254,0.00001060118,0.00005339392,0.0001275296,0.00001216432,0.001127338],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0171863,"threshold_uncertainty_score":0.03417248,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01119974871239666,"score_gpt":0.2236621091605266,"score_spread":0.2124623604481299,"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."}}