{"id":"W58025431","doi":"","title":"DB2 LUW optimizer: beginner to intermediate guide","year":2011,"lang":"en","type":"article","venue":"Conference of the Centre for Advanced Studies on Collaborative Research","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"IBM (Canada)","funders":"","keywords":"Computer science; Section (typography); SQL; Compiler; Programming language; Database; Heuristic; Information retrieval; Artificial intelligence; Operating system","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.007582936,0.0002013717,0.0004651423,0.0004364532,0.0006453561,0.0001868233,0.002657162,0.00003681309,0.0001215526],"category_scores_gemma":[0.03547899,0.0001153694,0.0001190764,0.003053732,0.0008134169,0.0002165992,0.002171838,0.0002002139,0.00015357],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001552189,"about_ca_system_score_gemma":0.0002791785,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008874579,"about_ca_topic_score_gemma":0.0002388721,"domain_scores_codex":[0.9950753,0.0006356931,0.000742126,0.0009696432,0.001957198,0.0006199782],"domain_scores_gemma":[0.9847029,0.003226309,0.0003221707,0.001795118,0.009773033,0.0001804991],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001798175,0.0004692542,0.0008016914,0.00006559431,0.0003265477,0.000007712399,0.04049046,0.003761165,0.001392809,0.09479729,0.6806867,0.1754026],"study_design_scores_gemma":[0.002286172,0.001384531,0.002005156,0.0007093907,0.00003288652,4.598689e-7,0.204503,0.003343757,0.05255087,0.08622281,0.6463836,0.0005773911],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5977191,0.00202831,0.04726676,0.08566214,0.02734952,0.02165245,0.003540328,0.0003155623,0.2144659],"genre_scores_gemma":[0.9260124,0.0001215,0.01263347,0.0002064927,0.00007813609,0.000183365,0.000004370082,0.00001920077,0.06074106],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3282934,"threshold_uncertainty_score":0.9726456,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.515187988773632,"score_gpt":0.52639787944863,"score_spread":0.011209890674998,"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."}}