{"id":"W2335204766","doi":"10.1061/41016(314)41","title":"Which Ground Motion Intensity Measure Is Most Appropriate for Conditioning Demand Models for Bridge Portfolios?","year":2008,"lang":"en","type":"article","venue":"Structures Congress 2008","topic":"Seismic Performance and Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministère de la Santé et des Services sociaux","keywords":"Spectral acceleration; Probabilistic logic; Measure (data warehouse); Computer science; Bridge (graph theory); Acceleration; Incremental Dynamic Analysis; Vulnerability (computing); Peak ground acceleration; Intensity (physics); Ground motion; Reliability engineering; Structural engineering; Data mining; 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":[0.00404526,0.0008430242,0.0009040078,0.0009134493,0.0002236749,0.001321693,0.001314567,0.001195319,0.001711307],"category_scores_gemma":[0.01263132,0.0003789414,0.001011098,0.0006607651,0.0008920885,0.002173915,0.001078029,0.001184655,0.0003709013],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006874596,"about_ca_system_score_gemma":0.0005882201,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001754263,"about_ca_topic_score_gemma":0.001880094,"domain_scores_codex":[0.9990477,0.0004048254,0.00005862767,0.0001686772,0.0001787294,0.0001413944],"domain_scores_gemma":[0.997271,0.001535838,0.0005409697,0.0002906077,0.0002555968,0.0001059977],"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":[0.0004046025,0.0004361324,0.03737166,0.0003480007,0.0001869128,0.0001827676,0.000252215,0.7980401,0.01827453,0.03208172,0.001934665,0.1104866],"study_design_scores_gemma":[0.00001483756,0.0001659727,0.006056402,0.00005336474,0.00004666818,0.00005908206,0.0001245877,0.9776874,0.004323996,0.01101664,0.0004175782,0.0000334309],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3467198,0.0002990997,0.6464539,0.001196322,0.00003663498,0.0002477466,0.000387085,0.0002590425,0.004400417],"genre_scores_gemma":[0.9473552,0.0003790891,0.05084847,0.0001907922,0.00005883399,0.0001198929,0.0003355998,0.00008846908,0.0006237085],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00404526,"threshold_uncertainty_score":0.02139366,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02388623516988646,"score_gpt":0.2337366035505657,"score_spread":0.2098503683806792,"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."}}