{"id":"W331118651","doi":"10.1007/978-3-642-44973-4_41","title":"Using Racing to Automatically Configure Algorithms for Scaling Performance","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Scaling; Computer science; Algorithm; Mathematics; Geometry","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":[0.001808023,0.001535416,0.0008933201,0.00160195,0.0006728519,0.001607698,0.002018498,0.001068304,0.00895251],"category_scores_gemma":[0.01219944,0.0008297402,0.0005812717,0.0008075136,0.0005172951,0.002704094,0.001269972,0.001171804,0.002777062],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007593969,"about_ca_system_score_gemma":0.0007653372,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001385126,"about_ca_topic_score_gemma":0.001942293,"domain_scores_codex":[0.998742,0.0002859726,0.0001064718,0.0003959262,0.0003459365,0.0001237145],"domain_scores_gemma":[0.9936668,0.002990203,0.0005047502,0.001544534,0.001044462,0.0002493298],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001025371,0.0004125081,0.01010915,0.0003049221,0.0001253945,0.0002007857,0.0003832121,0.08255087,0.0446225,0.005294358,0.01807183,0.8368991],"study_design_scores_gemma":[0.00005962079,0.0001376752,0.001489977,0.00003345897,0.00003099472,0.0001079315,0.00006383099,0.9581464,0.02693165,0.006101667,0.006859167,0.0000376317],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1058572,0.0004155437,0.811269,0.0002214579,0.0002581526,0.0002664552,0.000298177,0.06873475,0.01267936],"genre_scores_gemma":[0.5368311,0.0001299599,0.4517295,0.0001816592,0.00006023132,0.0002615758,0.0009849508,0.005754245,0.004066729],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00895251,"threshold_uncertainty_score":0.02994907,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06047288991167197,"score_gpt":0.3144251707782464,"score_spread":0.2539522808665744,"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."}}