{"id":"W3007378362","doi":"10.1145/3377555.3377892","title":"Improving database query performance with automatic fusion","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Compiler; Loop fusion; Query optimization; SQL; Sargable; Database; Fuse (electrical); Query language; Programming language; View; Loop unrolling; Search engine; Web search query; Information retrieval; Database design","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":[0.001814582,0.001038507,0.0008797575,0.0009824721,0.0006770803,0.001552296,0.001740437,0.0006140536,0.001779607],"category_scores_gemma":[0.005950184,0.0005979682,0.00102676,0.001931817,0.0009067367,0.002313712,0.001819618,0.001270866,0.000762158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009807906,"about_ca_system_score_gemma":0.002470199,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005111452,"about_ca_topic_score_gemma":0.003445308,"domain_scores_codex":[0.9972864,0.0003912245,0.000267221,0.000388869,0.001298344,0.000367927],"domain_scores_gemma":[0.9958055,0.001628432,0.0002839194,0.001125174,0.001075871,0.0000810862],"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":[0.00215725,0.0008215365,0.01588632,0.0003777621,0.0001980723,0.0004881379,0.001183375,0.1629234,0.2366576,0.01617554,0.01655317,0.5465779],"study_design_scores_gemma":[0.0001196915,0.0002244086,0.001961617,0.0000152223,0.00006696918,0.0001713973,0.0001427419,0.7989368,0.188415,0.004673981,0.00521046,0.00006171337],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2872925,0.0004070408,0.6488254,0.0003101635,0.00007066263,0.0001822039,0.0003859523,0.05682613,0.005700014],"genre_scores_gemma":[0.6964891,0.0001589236,0.297222,0.0002106713,0.00003418729,0.0001257963,0.001114264,0.00296405,0.001680977],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005111452,"threshold_uncertainty_score":0.01016343,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01440875879431176,"score_gpt":0.208022100579784,"score_spread":0.1936133417854723,"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."}}