{"id":"W2585764814","doi":"10.1371/journal.pone.0189866","title":"Mean field analysis of algorithms for scale-free networks in molecular biology","year":2017,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Algorithm; Scale-free network; Divergence (linguistics); Exponent; Complex network; Degree distribution; Computer science; Field (mathematics); Scale (ratio); Scaling; Biological network; Degree (music); Mathematics; Physics; Combinatorics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003243322,0.0007351927,0.0007531906,0.002372399,0.000926049,0.001487784,0.001405307,0.001647437,0.002871191],"category_scores_gemma":[0.02103064,0.0004375398,0.0009058689,0.001267249,0.001847476,0.003414464,0.0007910848,0.001624218,0.0003796966],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002728351,"about_ca_system_score_gemma":0.001052058,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0031106,"about_ca_topic_score_gemma":0.00227518,"domain_scores_codex":[0.9990774,0.0004217945,0.00004040616,0.0001569067,0.0002236452,0.00007975014],"domain_scores_gemma":[0.98763,0.01017626,0.0006421676,0.0004294415,0.0009243783,0.000197829],"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":[0.00004224728,0.00003498623,0.0009704894,0.0001343157,0.00005508785,0.00004673863,0.0001485247,0.5819748,0.001422515,0.3965101,0.001438915,0.01722137],"study_design_scores_gemma":[0.000004604376,0.000008987813,0.0001271158,0.00001053813,0.000004475838,0.00001258768,0.000008239269,0.88555,0.0002097497,0.1136528,0.0004033934,0.000007669362],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03260254,0.001073297,0.9621276,0.0006456927,0.00005222821,0.00005627491,0.00006870285,0.0002337057,0.003139861],"genre_scores_gemma":[0.764471,0.002219249,0.2227514,0.0004441186,0.0002123573,0.0004582023,0.00038946,0.0003599816,0.008694313],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003243322,"threshold_uncertainty_score":0.01979572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02087177627117628,"score_gpt":0.2597552842184117,"score_spread":0.2388835079472354,"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."}}