{"id":"W1565737841","doi":"10.1007/11788911_21","title":"Analyzing the Genetic Operations of an Evolutionary Query Optimizer","year":2006,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"IBM (Canada)","funders":"","keywords":"Crossover; Computer science; Genetic programming; Genetic algorithm; Plan (archaeology); Mutation; Operations research; Mathematical optimization; Artificial intelligence; Machine learning; Mathematics","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":[0.0007968708,0.0003853211,0.0004251056,0.0005835613,0.0004375444,0.0009597931,0.0009376121,0.0008700449,0.001808053],"category_scores_gemma":[0.004432307,0.0002951506,0.0004796652,0.000751846,0.0006883998,0.000936621,0.0003719497,0.0007269616,0.0002044389],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007913272,"about_ca_system_score_gemma":0.0007339141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006383051,"about_ca_topic_score_gemma":0.003199675,"domain_scores_codex":[0.9995061,0.0001160369,0.000018935,0.00006387023,0.0002144568,0.00008061443],"domain_scores_gemma":[0.9984502,0.001021521,0.0001136579,0.0001252704,0.0002524784,0.00003685078],"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":[0.0003437848,0.000167849,0.007890174,0.00008557191,0.00007338238,0.0002832831,0.0002503874,0.8177081,0.03404511,0.04622806,0.0007736139,0.09215063],"study_design_scores_gemma":[0.000009783567,0.00005136132,0.0006495909,0.000002979475,0.00001875569,0.00004014416,0.0000366616,0.9897371,0.003089548,0.006114617,0.0002438076,0.000005600096],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5574914,0.0002600267,0.4343354,0.0003063893,0.00003229039,0.00009345129,0.00006702175,0.0004836402,0.006930552],"genre_scores_gemma":[0.8637909,0.0001490721,0.1322919,0.00006726368,0.00001757696,0.00005335238,0.0001330001,0.000142323,0.003354627],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006383051,"threshold_uncertainty_score":0.0126918,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01167690734761856,"score_gpt":0.2383124345639564,"score_spread":0.2266355272163379,"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."}}