{"id":"W2102531694","doi":"10.1139/t01-021","title":"Terrain-based mapping of landslide susceptibility using a geographical information system: a case study","year":2001,"lang":"en","type":"article","venue":"Canadian Geotechnical Journal","topic":"Landslides and related hazards","field":"Environmental Science","cited_by":191,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Landslide; Terrain; Elevation (ballistics); Geology; Logistic regression; Geographic information system; Cartography; Regression analysis; Geography; Geomorphology; Remote sensing; Statistics; Geometry; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001210156,0.0002749549,0.0002362135,0.001701866,0.0008557457,0.0007976126,0.0006376706,0.00050795,0.000964537],"category_scores_gemma":[0.003635315,0.0001773121,0.000319768,0.003460985,0.0006962306,0.0006831117,0.0006173524,0.0003564954,0.0001226667],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00112269,"about_ca_system_score_gemma":0.0007737472,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03256164,"about_ca_topic_score_gemma":0.0551349,"domain_scores_codex":[0.9991266,0.0005139902,0.00004352805,0.00004676438,0.0001965205,0.00007269097],"domain_scores_gemma":[0.9982153,0.001137904,0.0001722596,0.0001625708,0.0002285772,0.00008335104],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007023351,0.001969819,0.5186932,0.0009812877,0.0002019924,0.02639124,0.02169449,0.144825,0.008828755,0.01488695,0.002866687,0.2579583],"study_design_scores_gemma":[0.0002483156,0.002213222,0.401268,0.0002366809,0.0002401855,0.008351766,0.05220867,0.4886656,0.02408936,0.004819234,0.0174717,0.0001872661],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9892858,0.00006880628,0.007853538,0.0001597371,0.000003190959,0.0001292883,0.0001659336,0.00004058112,0.002293233],"genre_scores_gemma":[0.9855872,0.0001349608,0.01375004,0.000005949697,0.000002598695,0.00004705671,0.0001031569,0.000005116794,0.0003639682],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03256164,"threshold_uncertainty_score":0.06474417,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01432103526944129,"score_gpt":0.2274578363842161,"score_spread":0.2131368011147748,"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."}}