{"id":"W2196655855","doi":"10.1103/physreve.92.062807","title":"General and exact approach to percolation on random graphs","year":2015,"lang":"en","type":"article","venue":"Physical Review E","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec – Nature et technologies; Canadian Institutes of Health Research","keywords":"Random graph; Percolation (cognitive psychology); Continuum percolation theory; Giant component; Statistical physics; Percolation threshold; Mathematics; Phase transition; Connected component; Discontinuity (linguistics); Combinatorics; Graph; Percolation critical exponents; Discrete mathematics; Critical exponent; Physics; Mathematical analysis","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":[],"consensus_categories":[],"category_scores_codex":[0.0001861682,0.0001236499,0.0003391805,0.00002902076,0.00003537176,0.00002562435,0.00008939453,0.000007105061,0.00001595989],"category_scores_gemma":[0.00001163895,0.00009499817,0.0001312059,0.0002285246,0.00001573003,0.00005644947,0.0000453791,0.00008586839,0.00003508541],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001299545,"about_ca_system_score_gemma":0.00001049308,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003360786,"about_ca_topic_score_gemma":3.312616e-7,"domain_scores_codex":[0.9992687,0.00008348859,0.0001356932,0.0002222211,0.0001602315,0.0001296846],"domain_scores_gemma":[0.999507,0.00003919664,0.00004514609,0.0002148759,0.00004493932,0.0001489117],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00007639368,0.001037638,0.005779296,0.0002030016,0.0001774946,4.738245e-7,0.0003436132,0.0003373772,0.0006787265,0.5953045,0.06376181,0.3322997],"study_design_scores_gemma":[0.004653518,0.0009636046,0.01479098,0.00224975,0.001356075,0.000002830449,0.00009923682,0.1242786,0.001055375,0.5148965,0.3332588,0.002394711],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7745513,0.005001075,0.05242595,0.001014235,0.00006201464,0.002297183,0.00001487191,0.0002013869,0.164432],"genre_scores_gemma":[0.9974399,0.0001066794,0.001511427,0.0003809927,0.000339091,0.0001140517,0.00003026229,0.00001094391,0.00006672012],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.329905,"threshold_uncertainty_score":0.3873913,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.035516383608069,"score_gpt":0.3251622333532186,"score_spread":0.2896458497451496,"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."}}