{"id":"W1545751276","doi":"10.1007/978-3-319-16112-9_16","title":"Inter-layer Degree Correlations in Heterogeneously Growing Multiplex Networks","year":2015,"lang":"en","type":"book-chapter","venue":"Studies in computational intelligence","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Degree (music); Layer (electronics); Homogeneous; Node (physics); Multiplex; Statistical physics; Focus (optics); Monte Carlo method; Mathematics; Computer science; Physics; Statistics; Materials science; Nanotechnology; Bioinformatics; Biology; Optics","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"],"consensus_categories":[],"category_scores_codex":[0.0004204266,0.0004886338,0.0007424987,0.0004493325,0.000116469,0.00003990184,0.000424326,0.0001385034,0.0002627995],"category_scores_gemma":[0.00003124055,0.0005260591,0.0002476696,0.0002163824,0.0002889259,0.0001465884,0.000561026,0.000767585,0.00006800366],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003732337,"about_ca_system_score_gemma":0.00008750873,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001428473,"about_ca_topic_score_gemma":0.0005529454,"domain_scores_codex":[0.9975231,0.00006214184,0.001037996,0.0006310933,0.0003564495,0.0003891783],"domain_scores_gemma":[0.9979643,0.0008487344,0.0003433926,0.0003128597,0.0004590282,0.00007173398],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001342512,0.00004245454,0.003712716,0.00001116021,0.0003032307,0.000022989,0.0004254756,0.799974,1.639686e-7,0.155906,0.001898941,0.03768945],"study_design_scores_gemma":[0.0001371093,0.00005017922,0.000138385,0.0007740314,0.00006233785,0.000004843183,0.0003782148,0.4389695,0.000003651303,0.5532178,0.005626401,0.0006375813],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0003534429,0.006127462,0.8878723,0.000136684,0.0007262873,0.0007342614,0.0000469381,0.0001181979,0.1038844],"genre_scores_gemma":[0.9816783,0.0001239363,0.007320439,0.00007484683,0.0005520515,0.00008873529,0.0002458999,0.0000673442,0.009848445],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9813249,"threshold_uncertainty_score":0.9997191,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2166275278247333,"score_gpt":0.3885169340212654,"score_spread":0.1718894061965321,"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."}}