{"id":"W4309121723","doi":"10.1061/9780784484449.036","title":"Applying Consequence-Driven Scenario Selection to Lifelines","year":2022,"lang":"en","type":"article","venue":"Lifelines 2022","topic":"earthquake and tectonic studies","field":"Earth and Planetary Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Induced seismicity; Vulnerability (computing); Stakeholder; Metric (unit); Critical infrastructure; Risk analysis (engineering); Computer science; Population; Seismic hazard; Hazard; Seismic risk; Vulnerability assessment; Event (particle physics); Engineering; Computer security; Business; Civil engineering; Psychological resilience; Operations management","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003038295,0.0001740677,0.0002224464,0.0001703888,0.001094698,0.0000511018,0.0002768607,0.0000311978,0.003891952],"category_scores_gemma":[0.0001321595,0.0001635308,0.00008186308,0.0008192706,0.00004709834,0.00009566435,0.00009170186,0.0002853039,0.0004276758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001472462,"about_ca_system_score_gemma":0.00012176,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002071533,"about_ca_topic_score_gemma":0.003691847,"domain_scores_codex":[0.9983326,0.0001249119,0.0002958866,0.0003962842,0.0004574336,0.0003929111],"domain_scores_gemma":[0.9993678,0.0001718837,0.00007807882,0.0001762576,0.00006854802,0.0001374251],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001335571,0.00004385754,0.7663298,0.00002203465,0.00008197805,0.0001004549,0.0006285406,0.08896913,0.0005670991,0.00006420394,0.03249561,0.1105637],"study_design_scores_gemma":[0.0005139969,0.0005929093,0.1289519,0.00001525611,0.00004170362,0.0002949056,0.001987864,0.02567163,0.0001892432,0.0004896363,0.8406468,0.0006041728],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9846108,0.002533866,0.000254175,0.004252913,0.002874265,0.001102855,0.000274987,0.000393382,0.003702785],"genre_scores_gemma":[0.9918865,0.00009729461,0.001366142,0.002797028,0.001626759,0.0001248464,0.0001141824,0.000008870189,0.0019784],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8081512,"threshold_uncertainty_score":0.9970186,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02518727468232497,"score_gpt":0.2393685252274137,"score_spread":0.2141812505450887,"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."}}