{"id":"W2302144074","doi":"10.1038/srep37057","title":"Graphlet characteristics in directed networks","year":2016,"lang":"en","type":"preprint","venue":"Scientific Reports","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; Montreal Heart Institute","funders":"Institut de Cardiologie de Montréal; Fondation Institut de Cardiologie de Montréal","keywords":"Centrality; Similarity (geometry); Vertex (graph theory); Correlation; Structural similarity; Network analysis; Node (physics); Computer science; Community structure; Complex network; Mathematics; Theoretical computer science; Artificial intelligence; Data mining; Combinatorics; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001342155,0.0003626082,0.0006691258,0.0004580079,0.0001535451,0.0004805731,0.0003797673,0.0001587034,0.00071496],"category_scores_gemma":[0.00002070247,0.0003118931,0.0003608498,0.0006271558,0.0002151871,0.00007435131,0.0008432957,0.0005564255,0.00001187413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007247635,"about_ca_system_score_gemma":0.0001697928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001378773,"about_ca_topic_score_gemma":0.0000319925,"domain_scores_codex":[0.9965211,0.0001110848,0.001088424,0.001356455,0.00040268,0.0005202576],"domain_scores_gemma":[0.9966466,0.00005092862,0.001008284,0.001954106,0.0002164979,0.0001235398],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000008879503,0.0002664293,0.8795332,0.00003688141,0.0002048555,0.0003796954,0.0001376908,0.0002308336,0.0004169128,0.001841572,0.08115475,0.03578831],"study_design_scores_gemma":[0.0001733255,0.00001191573,0.06750248,0.001154919,0.0001668291,0.00001150154,0.00001691992,0.01524044,0.0009414901,0.8463489,0.06700365,0.0014276],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.872793,0.0004805259,0.07339251,0.00022995,0.02513618,0.00170523,0.00007872533,0.001062811,0.02512103],"genre_scores_gemma":[0.9956408,0.000005885895,0.0005192538,0.000006884278,0.0007013016,0.0001349834,0.000876138,0.00003743342,0.002077284],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8445074,"threshold_uncertainty_score":0.9999333,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01101730971804185,"score_gpt":0.2551404158931408,"score_spread":0.2441231061750989,"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."}}