{"id":"W2001288841","doi":"10.1142/s1793431107000195","title":"USE OF GEOGRAPHIC INFORMATION SYSTEMS FOR GEOSEISMIC HAZARDS","year":2007,"lang":"en","type":"article","venue":"Journal of Earthquake and Tsunami","topic":"Seismic Performance and Analysis","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University at Buffalo; McMaster University","keywords":"Earthquake scenario; Probabilistic logic; Geographic information system; Seismic hazard; Seismic risk; Hazard; Computer science; Metropolitan area; Seismology; Spatial analysis; Ground motion; Information system; Geology; Data mining; Risk analysis (engineering); Geography; Remote sensing; Engineering; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.003273736,0.0007135413,0.0005837287,0.005379677,0.0007926351,0.004671481,0.001164554,0.0009270823,0.01197742],"category_scores_gemma":[0.01033393,0.0004275408,0.0007092705,0.009300167,0.0007906469,0.004362104,0.002884408,0.001160423,0.005413805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001239847,"about_ca_system_score_gemma":0.001725487,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005340104,"about_ca_topic_score_gemma":0.002945827,"domain_scores_codex":[0.9976193,0.001027018,0.0003074147,0.0002322449,0.0007337733,0.00008017693],"domain_scores_gemma":[0.9942637,0.00264461,0.0004857713,0.001459736,0.0009583937,0.0001878735],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008888028,0.00005139054,0.002920138,0.0009342642,0.0001609785,0.00037988,0.001167314,0.01119237,0.002060512,0.4069818,0.08223294,0.4918295],"study_design_scores_gemma":[0.00003284804,0.00004147743,0.001691291,0.0004172478,0.00008243518,0.0003795151,0.000312009,0.01953426,0.002270427,0.0987073,0.8764743,0.00005688073],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00582319,0.008822816,0.8575378,0.005451086,0.001033109,0.0004406728,0.01471573,0.02059083,0.08558472],"genre_scores_gemma":[0.1585533,0.0131172,0.7781836,0.00134222,0.000700286,0.0008498533,0.02762146,0.002035352,0.01759665],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01197742,"threshold_uncertainty_score":0.04006839,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0107676815757553,"score_gpt":0.2129739045500666,"score_spread":0.2022062229743113,"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."}}