{"id":"W2000719367","doi":"10.1109/icdmw.2009.18","title":"Semantic-Rich Markov Models for Web Prefetching","year":2009,"lang":"en","type":"article","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Semantics (computer science); Social Semantic Web; Semantic Web Stack; Markov chain; Semantic Web; Markov model; Data Web; Information retrieval; Context (archaeology); Markov process; Ontology; World Wide Web; Data mining; Web service; Machine learning; Programming language; Mathematics","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":[0.001213818,0.0007101044,0.001245876,0.001033351,0.000692254,0.0009673836,0.001446786,0.001323967,0.005642152],"category_scores_gemma":[0.006717674,0.0007571179,0.001107386,0.001300339,0.0007265959,0.002206902,0.0007708432,0.001629893,0.001039197],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001573208,"about_ca_system_score_gemma":0.001227286,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01895588,"about_ca_topic_score_gemma":0.03202919,"domain_scores_codex":[0.9994602,0.000225945,0.00002997891,0.00009422713,0.0001090823,0.00008051316],"domain_scores_gemma":[0.9958143,0.003266016,0.0002576659,0.0003177219,0.0002565251,0.00008776136],"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":[0.00009008029,0.00004276633,0.001040855,0.00004643441,0.00003682184,0.00006739358,0.00006594224,0.9309092,0.0005227998,0.05086005,0.001509543,0.01480803],"study_design_scores_gemma":[0.00000368612,0.000003933457,0.00005838516,0.000002163324,0.000003429445,0.00000448497,0.000002800001,0.9884163,0.00004943228,0.01129937,0.0001529727,0.000003089229],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04632584,0.0007056143,0.9471887,0.0006772515,0.0000731092,0.00007157955,0.00061421,0.000770698,0.003572905],"genre_scores_gemma":[0.8603975,0.0009418257,0.1258811,0.0002844514,0.00016323,0.0003226341,0.001286258,0.0001319211,0.01059112],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01895588,"threshold_uncertainty_score":0.03769112,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02930532787653242,"score_gpt":0.2682567642212813,"score_spread":0.2389514363447489,"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."}}