{"id":"W2981452035","doi":"10.4095/223221","title":"Revised deaggregation of seismic hazard for selected Canadian cities","year":2007,"lang":"en","type":"report","venue":"","topic":"Seismic Performance and Analysis","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Hazard; Forensic engineering; Engineering; Seismology; Geography; Geology; Chemistry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001205048,0.00136426,0.0006609956,0.006896068,0.001268962,0.00155347,0.001199224,0.0002233369,0.009531728],"category_scores_gemma":[0.004587684,0.0004369313,0.0008301546,0.009591394,0.0002898888,0.0005446808,0.001057094,0.0006689025,0.001210963],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02266368,"about_ca_system_score_gemma":0.01911596,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9612393,"about_ca_topic_score_gemma":0.9763561,"domain_scores_codex":[0.9982418,0.0000770045,0.0001210259,0.0001575582,0.001114173,0.0002884153],"domain_scores_gemma":[0.994724,0.0002260269,0.0002526457,0.0002703723,0.004409232,0.0001176134],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006155588,0.0001758547,0.3566455,0.0008033819,0.0004776758,0.0004673698,0.001671545,0.1468336,0.006122594,0.01982192,0.211996,0.2543689],"study_design_scores_gemma":[0.00007731207,0.0001001802,0.7548817,0.00009878336,0.0001560252,0.0002156559,0.00239063,0.05940868,0.004902038,0.00269721,0.1748885,0.0001833308],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.4030637,0.0006971867,0.03490191,0.0007036999,0.0001472264,0.00153109,0.4484417,0.002380586,0.1081328],"genre_scores_gemma":[0.572152,0.0009829862,0.05124152,0.000138558,0.00002950496,0.0006241439,0.3388445,0.0004794796,0.03550724],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03876066,"threshold_uncertainty_score":0.1644373,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01985125953700931,"score_gpt":0.2478714364887918,"score_spread":0.2280201769517825,"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."}}