{"id":"W2031920071","doi":"10.1145/2492517.2492662","title":"Detect inflated follower numbers in OSN using star sampling","year":2013,"lang":"en","type":"article","venue":"","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Simple random sample; Sampling (signal processing); Estimator; Computer science; Random walk; Variance (accounting); Systematic sampling; Star (game theory); Sample (material); Simple (philosophy); Statistics; Poisson sampling; Sampling design; Importance sampling; Algorithm; Mathematics; Slice sampling; Monte Carlo method; Telecommunications","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00009678097,0.0001234665,0.0001835569,0.0001019234,0.00005085645,0.0000663248,0.0001196237,0.00002864218,0.00418312],"category_scores_gemma":[0.00000241638,0.0001151408,0.00009938471,0.0003466231,0.0000162399,0.0001675703,0.00006953939,0.0001247532,0.00006292608],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004078415,"about_ca_system_score_gemma":0.00001929676,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01158488,"about_ca_topic_score_gemma":0.0002112674,"domain_scores_codex":[0.9991926,0.00002776239,0.0002439792,0.0001865096,0.00009069894,0.0002584827],"domain_scores_gemma":[0.9995939,0.00004244038,0.00005794299,0.000213685,0.00004283276,0.00004926432],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001801428,0.0002181935,0.8409163,0.00001163912,0.0004441433,0.000003490885,0.0004903302,0.007591785,0.05691134,0.007514119,0.003504398,0.08237619],"study_design_scores_gemma":[0.002653667,0.0001290893,0.09099798,0.0002899418,0.0002513467,0.00000226374,0.001642176,0.6693765,0.03485809,0.181908,0.01504528,0.00284565],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.940878,0.00001075022,0.04836617,0.0000203484,0.00002163834,0.0001823734,7.50733e-7,0.00008639814,0.01043361],"genre_scores_gemma":[0.9697824,4.596066e-7,0.0299031,0.00003217866,0.0000677698,0.00001737748,0.000007441213,0.00001577249,0.0001735111],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7499184,"threshold_uncertainty_score":0.9967272,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02242390492234059,"score_gpt":0.2864961626567138,"score_spread":0.2640722577343732,"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."}}