{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007627502,0.001670253,0.0008845243,0.003290383,0.001162075,0.003951152,0.003173734,0.001614059,0.009317629],"category_scores_gemma":[0.02675383,0.0008400556,0.002560175,0.002333045,0.002805554,0.0045407,0.004553413,0.003113279,0.0009120145],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002972874,"about_ca_system_score_gemma":0.00329082,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009565085,"about_ca_topic_score_gemma":0.01254053,"domain_scores_codex":[0.9929112,0.003652754,0.0003822958,0.001040241,0.001613601,0.0003998349],"domain_scores_gemma":[0.9844209,0.01056463,0.001159616,0.001497751,0.001753049,0.0006039626],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000122468,0.0001538109,0.004552014,0.0003068599,0.0001838939,0.000991155,0.0007279983,0.5968783,0.001005992,0.3179403,0.004563872,0.07257333],"study_design_scores_gemma":[0.00003957142,0.00006369311,0.0004623658,0.0000813603,0.00004746379,0.0002150572,0.0002274994,0.7161457,0.0007601289,0.2669584,0.01495659,0.0000421234],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005008164,0.000158697,0.9852313,0.0006110352,0.00005069098,0.0003278255,0.0004348445,0.0004470759,0.007730417],"genre_scores_gemma":[0.2474776,0.0003747814,0.7462655,0.0003457106,0.0001184072,0.0006594986,0.001375284,0.0002789038,0.003104365],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009565085,"threshold_uncertainty_score":0.04033858,"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."}}