{"id":"W2074449096","doi":"10.1007/s10518-013-9495-7","title":"Developing and testing the Automated Post-Event Earthquake Loss Estimation and Visualisation (APE-ELEV) technique","year":2013,"lang":"en","type":"article","venue":"Bulletin of Earthquake Engineering","topic":"earthquake and tectonic studies","field":"Earth and Planetary Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Visualization; Event (particle physics); Estimation; Computer science; Real-time computing; Data visualization; Seismology; Data mining; Geology; Engineering; Systems engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.0009539438,0.0004417523,0.0003711956,0.0005015912,0.0002189361,0.0006129602,0.001255663,0.0008772655,0.004046088],"category_scores_gemma":[0.004077526,0.0002678986,0.0003136299,0.0003724457,0.0002638612,0.0009735288,0.0009505213,0.0005672576,0.001150061],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001833175,"about_ca_system_score_gemma":0.0004609496,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002764832,"about_ca_topic_score_gemma":0.002370323,"domain_scores_codex":[0.9992732,0.0001578554,0.00003786683,0.0001621278,0.0003016257,0.00006727561],"domain_scores_gemma":[0.9982349,0.000955402,0.00008865089,0.0002740804,0.000379883,0.00006715265],"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":[0.001223059,0.0006949747,0.01359825,0.0002911777,0.0001410781,0.0004880073,0.000422046,0.1083188,0.1322701,0.003253257,0.006836663,0.7324625],"study_design_scores_gemma":[0.0001265076,0.0004087981,0.005996092,0.00001563217,0.000029224,0.0003193113,0.00008571105,0.9195281,0.06821193,0.0007323684,0.004516091,0.00003023814],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3227718,0.0001977162,0.6430847,0.0001885342,0.000109429,0.0002244391,0.0005810514,0.02965593,0.003186299],"genre_scores_gemma":[0.5609014,0.00008410383,0.4346659,0.00008786008,0.00002935998,0.00008773466,0.0008130249,0.0005579459,0.002772683],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004046088,"threshold_uncertainty_score":0.0135355,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01521309068702609,"score_gpt":0.2175797433051197,"score_spread":0.2023666526180936,"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."}}