{"id":"W4234371023","doi":"10.31224/osf.io/yp2j7","title":"Critical Flow Centrality Measures on Interdependent Networks with Time-Varying Demands","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Water Systems and Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Interdependence; Reliability (semiconductor); Geospatial analysis; Critical infrastructure; Computer science; Centrality; Component (thermodynamics); Flow (mathematics); Measure (data warehouse); Flow network; Enhanced Data Rates for GSM Evolution; Interdependent networks; Electricity; Scale (ratio); Cascading failure; Operations research; Risk analysis (engineering); Distributed computing; Complex network; Data mining; Electric power system; Engineering; Business; Mathematical optimization; Geography; Computer security","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":[0.002156017,0.0008652828,0.0005201151,0.005527183,0.0006263076,0.001267892,0.0008682535,0.0006885726,0.002206263],"category_scores_gemma":[0.01306562,0.0002815561,0.0004397224,0.002467206,0.001971882,0.003236825,0.001125533,0.00106567,0.0001420425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001702385,"about_ca_system_score_gemma":0.0004580231,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002882115,"about_ca_topic_score_gemma":0.002552327,"domain_scores_codex":[0.9988334,0.0004490086,0.00004456718,0.0002418263,0.000326448,0.0001047419],"domain_scores_gemma":[0.9862862,0.01018155,0.001537163,0.000509713,0.0009758471,0.0005094637],"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":[0.0001768937,0.00006246401,0.009488564,0.0001577538,0.0001197162,0.0002410035,0.000505954,0.6063905,0.004777203,0.3181034,0.002213778,0.05776278],"study_design_scores_gemma":[0.00001367498,0.00005250437,0.003636111,0.00003294443,0.00003069524,0.0001186468,0.0001541179,0.8573872,0.001273293,0.1342777,0.002993209,0.00003005071],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1187414,0.0005603962,0.8743504,0.0003111491,0.00004584167,0.00006583289,0.0002374759,0.0001651282,0.005522326],"genre_scores_gemma":[0.9004502,0.0005471933,0.09603908,0.00006459224,0.0001779798,0.0001209719,0.000285921,0.0001075539,0.002206547],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005527183,"threshold_uncertainty_score":0.01235169,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01215788492864119,"score_gpt":0.2139682466652397,"score_spread":0.2018103617365985,"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."}}