{"id":"W2006536984","doi":"10.1145/1183614.1183737","title":"Constructing better document and query models with markov chains","year":2006,"lang":"en","type":"article","venue":"","topic":"Web Data Mining and Analysis","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Markov chain; Information retrieval; Query optimization; Theoretical computer science; Artificial intelligence; Machine learning","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":[0.0001144812,0.00007454033,0.00009362995,0.00005771927,0.00006711359,0.0001974321,0.0001741863,0.00001705138,0.00001047214],"category_scores_gemma":[0.000001430428,0.00005231747,0.00001586666,0.0001354691,0.00003927802,0.0004686468,0.0001171362,0.00004392274,0.000003351543],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001172801,"about_ca_system_score_gemma":0.00001437674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005901848,"about_ca_topic_score_gemma":0.0001473186,"domain_scores_codex":[0.9993654,0.00001684763,0.0001005813,0.0002447138,0.0001249647,0.0001474991],"domain_scores_gemma":[0.9996317,0.00002878463,0.00003448687,0.0002510639,0.00001925146,0.00003472322],"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":[0.00000388702,0.00003096462,0.03438188,0.00001486491,0.00006594723,0.00004781284,0.0001963367,0.000582229,0.0001350031,0.8134859,0.003650693,0.1474044],"study_design_scores_gemma":[0.0007239917,0.00007349563,0.001986607,0.00005429587,0.00003888845,0.0001453024,0.0003414732,0.9779047,0.001439289,0.01579687,0.0009659276,0.000529094],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1513074,0.00002137549,0.8302091,0.001524734,0.00001849285,0.00002481317,0.000001588069,0.00008059449,0.01681194],"genre_scores_gemma":[0.7376075,0.00000130695,0.2611785,0.000272726,0.00003072869,0.000002032598,0.00000350698,0.000002641344,0.0009010368],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9773225,"threshold_uncertainty_score":0.2133444,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006209587728475849,"score_gpt":0.1883046058453408,"score_spread":0.182095018116865,"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."}}