{"id":"W2943682469","doi":"10.1007/978-981-13-7446-3_7","title":"Resilience-Based Design for Blast Risk Mitigation: Learning from Natural Disasters","year":2019,"lang":"en","type":"book-chapter","venue":"","topic":"Infrastructure Resilience and Vulnerability Analysis","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"","keywords":"Resilience (materials science); Risk analysis (engineering); Natural disaster; Hazard; Downtime; Engineering; Process (computing); Emergency management; Computer science; Business; Reliability engineering; Geography; Economics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001948206,0.0004524273,0.000520403,0.0001781397,0.0001654555,0.00009555744,0.0002828475,0.0003899793,0.001066955],"category_scores_gemma":[0.00008663735,0.0004056035,0.0003942131,0.00005460636,0.0001177182,0.0001529764,0.00002108725,0.000780553,0.0001680067],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000161462,"about_ca_system_score_gemma":0.0001080914,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003321295,"about_ca_topic_score_gemma":0.00009616133,"domain_scores_codex":[0.9984027,0.00003775951,0.0004156721,0.0004975668,0.000320564,0.000325701],"domain_scores_gemma":[0.9984947,0.0007530306,0.0001390442,0.0004266937,0.0001006223,0.00008594026],"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":[0.00003593877,0.000001957937,0.000113288,0.0000732367,0.0001787143,0.000001019367,0.00007186567,0.9911539,0.0001920948,0.001284676,0.001247384,0.005645906],"study_design_scores_gemma":[0.0005191602,0.0001067651,0.0001258461,0.0002068658,0.0004087286,6.430349e-7,0.0001049725,0.9657818,0.002673216,0.007834649,0.02135806,0.0008792994],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001524047,0.000555834,0.9120943,0.0000536197,0.0007721078,0.0008250282,0.0001397452,0.000399539,0.08363577],"genre_scores_gemma":[0.6905706,0.0001740658,0.04153679,0.0001683228,0.000949517,0.00006005297,0.001249905,0.0002783584,0.2650124],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8705575,"threshold_uncertainty_score":0.9998462,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009136967390563683,"score_gpt":0.2007887680622017,"score_spread":0.191651800671638,"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."}}