{"id":"W2081401338","doi":"10.1145/1571941.1572146","title":"Using dynamic markov compression to detect vandalism in the wikipedia","year":2009,"lang":"en","type":"article","venue":"","topic":"Wikis in Education and Collaboration","field":"Social Sciences","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Compression (physics); Markov chain; Data compression; Markov process; Hidden Markov model; Markov model; Data mining; Machine learning; Dynamic range compression; Artificial intelligence; Mathematics; Statistics; Telecommunications","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001148323,0.000506132,0.000533946,0.00366312,0.0006068138,0.001091103,0.0006290124,0.0008383013,0.000620114],"category_scores_gemma":[0.009747724,0.0002099075,0.0003591404,0.001851829,0.0005163889,0.001468897,0.0006016659,0.0006349973,0.0003973059],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004589468,"about_ca_system_score_gemma":0.0008639292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009044283,"about_ca_topic_score_gemma":0.00706704,"domain_scores_codex":[0.9989518,0.0002556036,0.00006457612,0.00021109,0.0004163795,0.0001005541],"domain_scores_gemma":[0.9912966,0.005522547,0.001007572,0.0007755369,0.00119283,0.0002049449],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008065865,0.0007854952,0.1272708,0.0004193449,0.0002855675,0.0009731843,0.0008101292,0.110091,0.02645617,0.004482325,0.01315906,0.7144603],"study_design_scores_gemma":[0.00002052682,0.0001236525,0.0167613,0.00002508971,0.00005502143,0.0004667211,0.00011144,0.9610903,0.01655041,0.003333367,0.001412794,0.00004954392],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7999378,0.001086891,0.1835057,0.0005122646,0.0002579036,0.0002558178,0.001141824,0.00714651,0.006155168],"genre_scores_gemma":[0.954026,0.0002398363,0.04311514,0.00005650955,0.0001187842,0.0000528785,0.001254344,0.0001006363,0.001035817],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009044283,"threshold_uncertainty_score":0.01798332,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03020876314626032,"score_gpt":0.3991826432805757,"score_spread":0.3689738801343154,"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."}}