{"id":"W4245823248","doi":"10.1145/1114244.1114250","title":"Optimization of query streams using semantic prefetching","year":2005,"lang":"en","type":"article","venue":"ACM Transactions on Database Systems","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Latency (audio); Context (archaeology); Server; Query optimization; STREAMS; Data mining; Information retrieval; Database; Computer network","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":[0.0003586616,0.0002205491,0.0003170143,0.0002617272,0.000240428,0.00005678791,0.0005715459,0.00006488009,0.00001949445],"category_scores_gemma":[0.00004167211,0.0002058556,0.00008649836,0.0004736237,0.00004489777,0.001947989,0.0000400881,0.0001710344,0.00001973159],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001055091,"about_ca_system_score_gemma":0.00009011477,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008427167,"about_ca_topic_score_gemma":0.00008003106,"domain_scores_codex":[0.9981174,0.0001482074,0.0005933121,0.0004689063,0.0003900004,0.0002821474],"domain_scores_gemma":[0.9974688,0.0001516804,0.0002625944,0.001907613,0.0001037512,0.0001055718],"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":[0.00001159804,0.0001301152,0.00002452559,0.000170634,0.00004264633,0.000005240195,0.0002501649,0.978129,0.002674367,0.005745394,0.00002974633,0.01278663],"study_design_scores_gemma":[0.0004287248,0.00006001836,0.000007439775,0.0006041995,0.00003165713,0.00009179618,0.0002835589,0.9860477,0.007423284,0.00001180707,0.004711067,0.000298771],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005187922,0.0002388055,0.992754,0.00008781331,0.000634576,0.0003730972,0.0004226894,0.0001840118,0.0001170184],"genre_scores_gemma":[0.5395878,0.00005879476,0.4600142,0.00002725171,0.0001004866,0.00002803558,0.00006343139,0.00002188362,0.00009808865],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5343999,"threshold_uncertainty_score":0.8394548,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03100413104897517,"score_gpt":0.2743439780064526,"score_spread":0.2433398469574774,"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."}}