{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003097309,0.0000988716,0.0001379027,0.00006952069,0.00008451327,0.0001774476,0.0005602311,0.00004709128,0.000004036516],"category_scores_gemma":[0.000005372058,0.00007720855,0.0000611432,0.000119206,0.000003312183,0.0005888564,0.00006961705,0.00005078454,0.000004457098],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001760032,"about_ca_system_score_gemma":0.00002336303,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001666232,"about_ca_topic_score_gemma":0.000006496886,"domain_scores_codex":[0.9991827,0.00002255003,0.0001882625,0.0002666252,0.0001180356,0.000221806],"domain_scores_gemma":[0.9993912,0.00004368699,0.00004726578,0.0004215341,0.00004581663,0.0000504961],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003144888,0.00006924752,0.00005505281,0.00002242521,0.00001246775,0.000002371147,0.000403086,0.00004690396,0.001224226,0.7300639,0.04773521,0.220362],"study_design_scores_gemma":[0.0002166896,0.0001496675,0.0001688535,0.00002569982,0.000002883571,0.00001478171,0.00001223367,0.7817173,0.003532051,0.202509,0.01144479,0.0002060686],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001543903,0.00004853353,0.9332423,0.002570404,0.0001434809,0.0003059874,6.242853e-7,0.0005567287,0.061588],"genre_scores_gemma":[0.7048333,0.000005909394,0.2934106,0.000655723,0.00004287311,0.00001929936,5.173931e-7,0.000003914111,0.001027881],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7816704,"threshold_uncertainty_score":0.3148473,"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."}}