{"id":"W1992774762","doi":"10.1109/ciss.2014.6814136","title":"Microscopic generative models for complex networks","year":2014,"lang":"en","type":"article","venue":"","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Complex network; Generative grammar; Computer science; Human dynamics; Complex system; Evolving networks; Generative model; The Internet; Enhanced Data Rates for GSM Evolution; Data science; Theoretical computer science; Phenomenon; Small-world network; Artificial intelligence; World Wide Web; Epistemology","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":[],"consensus_categories":[],"category_scores_codex":[0.0001125647,0.0001262305,0.0002098229,0.00003069397,0.0001296399,0.00005390533,0.0001504657,0.00002277501,0.0004258116],"category_scores_gemma":[8.744233e-7,0.0001112813,0.0001335969,0.00008722804,0.00002906817,0.00006467657,0.00004979782,0.00005660218,0.000005902516],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001021602,"about_ca_system_score_gemma":0.000007817372,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000809885,"about_ca_topic_score_gemma":0.00001840164,"domain_scores_codex":[0.9992993,0.00003346135,0.0001737417,0.0002146234,0.00005267282,0.000226233],"domain_scores_gemma":[0.9995064,0.00007930037,0.00005818568,0.0002367942,0.00007006234,0.00004931637],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000007844078,0.00006914201,0.002855481,0.000003245034,0.0001198182,2.524088e-8,0.00004338043,0.110123,0.001334938,0.7753102,0.08259364,0.02753928],"study_design_scores_gemma":[0.000180351,0.00002791279,0.00009634803,0.000003203768,0.00002203753,3.953957e-8,0.00001125482,0.879235,0.00100936,0.1051389,0.01415144,0.0001242191],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002366929,0.00001378712,0.966948,0.000101706,0.00003107505,0.0002251504,0.00000562431,0.00008724735,0.03022049],"genre_scores_gemma":[0.8985096,6.523438e-7,0.09945939,0.0002540447,0.0005790627,0.00008637616,0.0001061614,0.00001561222,0.0009891605],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8961426,"threshold_uncertainty_score":0.4662338,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02714076824587968,"score_gpt":0.2832568401434916,"score_spread":0.2561160718976119,"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."}}