{"id":"W4319659049","doi":"10.1007/s11069-023-05836-y","title":"Flood, landslides, forest fire, and earthquake susceptibility maps using machine learning techniques and their combination","year":2023,"lang":"en","type":"article","venue":"Natural Hazards","topic":"Landslides and related hazards","field":"Environmental Science","cited_by":62,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"Iran National Science Foundation","keywords":"Natural hazard; Landslide; Topographic Wetness Index; Normalized Difference Vegetation Index; Flood myth; Environmental science; Hazard; Random forest; Geographic information system; Hydrology (agriculture); Cartography; Geography; Geology; Meteorology; Machine learning; Geotechnical engineering; Computer science; Climate change","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004607935,0.0001787324,0.0001821052,0.00005024263,0.0003251994,0.00006919323,0.00009211219,0.0001594946,0.00007993512],"category_scores_gemma":[0.00004928408,0.0001210856,0.0000457147,0.0003103244,0.0001630122,0.0002209139,0.0002706978,0.0004092227,0.000022471],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006524478,"about_ca_system_score_gemma":0.000008092905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001022909,"about_ca_topic_score_gemma":0.000708038,"domain_scores_codex":[0.998933,0.00007156791,0.0001753143,0.0003365693,0.0002014464,0.0002820568],"domain_scores_gemma":[0.9996322,0.00006585911,0.00006225632,0.0001380667,0.00001382694,0.00008783137],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007207591,0.00004193553,0.7871069,0.00005109313,0.00003191717,0.00003854645,0.0005433975,0.0002610175,0.006745999,0.0001147484,0.0004055652,0.2045868],"study_design_scores_gemma":[0.001489847,0.0003685835,0.6429501,0.0001080549,0.00004458989,0.0002198167,0.0002548386,0.308213,0.005582701,0.003631253,0.03646283,0.0006743991],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977131,0.000676762,0.00001079599,0.0002325266,0.0001038656,0.0002024367,0.00001461956,0.0002398441,0.0008059745],"genre_scores_gemma":[0.9981759,0.0004982296,0.0003680883,0.00003899892,0.00003838132,0.000004563956,0.00008114679,0.00001891931,0.0007758307],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.307952,"threshold_uncertainty_score":0.4937725,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00874074083712759,"score_gpt":0.2292697508722296,"score_spread":0.2205290100351021,"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."}}