{"id":"W2123061300","doi":"10.1145/1007568.1007591","title":"Optimization of query streams using semantic prefetching","year":2004,"lang":"en","type":"article","venue":"","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Latency (audio); Context (archaeology); Server; Query optimization; Data mining; Information retrieval; Computer network; Database","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":[0.00007698846,0.0000589792,0.00009389612,0.00004634665,0.00004954674,0.00001499781,0.0001212938,0.00001812003,0.000005383416],"category_scores_gemma":[0.00001441321,0.00004881862,0.00002232957,0.0001599032,0.00001630227,0.0008010439,0.00009506918,0.00002829211,0.000001879975],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002339444,"about_ca_system_score_gemma":0.00004797502,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004480597,"about_ca_topic_score_gemma":0.00002495447,"domain_scores_codex":[0.9994717,0.00001314054,0.0001634362,0.0001435473,0.0001098104,0.00009842046],"domain_scores_gemma":[0.999566,0.00001272593,0.00007784809,0.000277416,0.00004021653,0.00002583068],"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":[7.278036e-7,0.0000150547,0.000117631,0.00001999633,0.000003622962,0.000001970311,0.0001915823,0.8122041,0.001555902,0.1843199,0.0000017123,0.001567809],"study_design_scores_gemma":[0.0004132301,0.00004898406,0.0001193742,0.0002106683,0.000005665067,0.00003484104,0.0001820902,0.9670869,0.02938548,0.002115897,0.0001901299,0.0002067562],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03367201,0.00003195195,0.9654275,0.00003621375,0.000104487,0.00006447641,0.000001715696,0.0000675104,0.0005941616],"genre_scores_gemma":[0.3832291,0.000003825119,0.61671,0.00001739307,0.00001322875,6.958023e-7,0.000001791252,0.000002851879,0.00002112703],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3495571,"threshold_uncertainty_score":0.1990765,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01914326586735384,"score_gpt":0.254980061574268,"score_spread":0.2358367957069142,"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."}}