{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000450812,0.00007468988,0.0002142643,0.0003615083,0.00003064607,0.00003989247,0.00004954098,0.0000551114,0.000002450152],"category_scores_gemma":[0.00002216455,0.00006166002,0.0001140893,0.0001377255,0.00002003325,0.0007328523,0.000004836519,0.00009027555,0.000001231678],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000798732,"about_ca_system_score_gemma":0.00001276011,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002972433,"about_ca_topic_score_gemma":0.000005847504,"domain_scores_codex":[0.9991993,0.000004892333,0.0004914906,0.00002822128,0.0001407617,0.0001352779],"domain_scores_gemma":[0.9994876,0.00005891844,0.000170976,0.0000681988,0.0001556608,0.00005861393],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003424707,0.00009470001,0.1375232,0.001642333,0.001559112,0.00001868543,0.001835145,0.2993341,0.003449713,0.001022118,0.004031436,0.5491469],"study_design_scores_gemma":[0.001757322,0.0004618051,0.2673586,0.0003847576,0.0003244848,0.0002102476,0.0008387491,0.4801821,0.001435672,0.0001502539,0.2465366,0.0003594949],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9731695,0.0009917049,0.02542197,0.00001428954,0.000218383,0.00005568701,0.000009317256,0.00001075362,0.000108358],"genre_scores_gemma":[0.9982284,0.001298731,0.0002937387,0.00005108134,0.00009619426,6.941665e-7,0.000004245736,0.00000579633,0.00002113067],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5487874,"threshold_uncertainty_score":0.2514423,"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."}}