{"id":"W3212019433","doi":"10.32920/ryerson.14653833.v1","title":"Routing simulation of brain network topology.","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Routing table; Computer science; Static routing; Computer network; Equal-cost multi-path routing; Dynamic Source Routing; Link-state routing protocol; Routing (electronic design automation); Metrics; Policy-based routing; Distributed computing; Network packet; Routing protocol","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":[0.0002734971,0.0003259948,0.0002550441,0.0002820322,0.0002164858,0.0004598421,0.0007357386,0.0006770013,0.003456909],"category_scores_gemma":[0.00237059,0.0001723584,0.0003529362,0.0002870114,0.0003101087,0.0006076182,0.0004405836,0.0005387348,0.0002739214],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005266108,"about_ca_system_score_gemma":0.0005287799,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006521909,"about_ca_topic_score_gemma":0.004646977,"domain_scores_codex":[0.999884,0.0000449188,0.000005142797,0.00001831879,0.00003038326,0.0000172198],"domain_scores_gemma":[0.9993016,0.0004710605,0.00004604945,0.00006548424,0.00008186131,0.00003394237],"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":[0.00005350068,0.00002663788,0.0008987397,0.00003873623,0.0000186618,0.00006443827,0.00004596205,0.9861054,0.002613375,0.006063511,0.0004595093,0.003611536],"study_design_scores_gemma":[0.000008799865,0.00001485707,0.0001507024,0.00000204281,0.000003916287,0.00001232893,0.000009026926,0.9971855,0.0005927437,0.001583874,0.0004340789,0.000001950042],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6269194,0.0004467064,0.3363134,0.001205893,0.0002164276,0.0002754079,0.002233831,0.00129041,0.03109843],"genre_scores_gemma":[0.9523178,0.000262631,0.04187739,0.0001095981,0.00001449418,0.0002073102,0.0005561112,0.00008410893,0.004570399],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006521909,"threshold_uncertainty_score":0.01296788,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06724678647992388,"score_gpt":0.3187455532286537,"score_spread":0.2514987667487298,"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."}}