{"id":"W2110439290","doi":"10.1142/s0218001402002179","title":"IMPROVING ENCARTA SEARCH ENGINE PERFORMANCE BY MINING USER LOGS","year":2002,"lang":"en","type":"article","venue":"International Journal of Pattern Recognition and Artificial Intelligence","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick; Western University","funders":"","keywords":"Computer science; Web query classification; Web search query; Information retrieval; Search engine; Query expansion; Search-oriented architecture; Sargable; Query language; Query optimization; Spatial query; Web search engine; Cache; Data mining; World Wide Web","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":[0.00296459,0.001380574,0.001938617,0.003650754,0.0005987161,0.002300179,0.002042244,0.0006106892,0.001081483],"category_scores_gemma":[0.01390074,0.000640948,0.0006097543,0.003667678,0.0004174747,0.005101834,0.001103855,0.0009794515,0.00140959],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005648932,"about_ca_system_score_gemma":0.001734057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009829563,"about_ca_topic_score_gemma":0.01483977,"domain_scores_codex":[0.9977995,0.0004340351,0.0002292343,0.0004124705,0.0009880229,0.0001368537],"domain_scores_gemma":[0.9891159,0.004173558,0.0007671859,0.003076327,0.002486991,0.0003801048],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002505938,0.001312221,0.08676758,0.0006245577,0.000582668,0.000408021,0.0006663686,0.04342497,0.04462574,0.00365451,0.01706859,0.7983589],"study_design_scores_gemma":[0.00009945459,0.0004092969,0.01064055,0.00002430165,0.0001321725,0.0003805851,0.0001606552,0.9459141,0.03383442,0.002828988,0.005486821,0.00008862596],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5671809,0.003044987,0.3202001,0.001169179,0.000133097,0.0004136391,0.003366437,0.09865392,0.005837649],"genre_scores_gemma":[0.7029753,0.0005611709,0.2867676,0.000184076,0.00008969085,0.0001584318,0.005731916,0.0008116416,0.00272018],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009829563,"threshold_uncertainty_score":0.01954466,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0828712349594512,"score_gpt":0.2874540315330467,"score_spread":0.2045827965735955,"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."}}