{"id":"W2991027952","doi":"10.1111/coin.12249","title":"CSBF: A static ensemble fusion method based on the centrality score of complex networks","year":2019,"lang":"en","type":"article","venue":"Computational Intelligence","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Centrality; Pairwise comparison; Artificial intelligence; Ensemble learning; Computer science; Classifier (UML); Weighting; Fusion; Machine learning; Pattern recognition (psychology); Data mining; Mathematics; Statistics","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":[0.001926857,0.001115157,0.001289767,0.003470381,0.0009860384,0.001395819,0.001350677,0.001113607,0.001918938],"category_scores_gemma":[0.004457169,0.0003219914,0.001142772,0.00200693,0.0004669546,0.001945897,0.001344483,0.0009208377,0.0005904889],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009969581,"about_ca_system_score_gemma":0.0009089586,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006478014,"about_ca_topic_score_gemma":0.005254715,"domain_scores_codex":[0.9989247,0.0002164637,0.00005807314,0.0002654215,0.0004328479,0.0001025029],"domain_scores_gemma":[0.9984524,0.0004373881,0.0001602456,0.0001488217,0.0006989204,0.0001022034],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002681285,0.0001330296,0.004300694,0.00011452,0.0002667873,0.0001427989,0.0002085488,0.3781649,0.0137654,0.01160767,0.004659899,0.5863677],"study_design_scores_gemma":[0.000004823488,0.00002099506,0.0005210689,0.000005429,0.0000244629,0.00002520773,0.00001616287,0.994555,0.001258918,0.002902286,0.000654471,0.00001119732],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0219512,0.0003203418,0.9757183,0.0001240139,0.00007924199,0.00004187244,0.00007609604,0.0006145964,0.001074405],"genre_scores_gemma":[0.6740848,0.0004331979,0.3220614,0.0001004289,0.0002237623,0.0001547443,0.0004916255,0.0001684049,0.002281688],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006478014,"threshold_uncertainty_score":0.01288062,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04642834771364663,"score_gpt":0.3273409589880015,"score_spread":0.2809126112743549,"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."}}