{"id":"W1963759559","doi":"10.1145/2463372.2463438","title":"Ordered racing protocols for automatically configuring algorithms for scaling performance","year":2013,"lang":"en","type":"article","venue":"","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Scaling; Algorithm; 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":[0.01352368,0.001704156,0.001372564,0.002303887,0.001700329,0.002729183,0.004467248,0.001697713,0.008742636],"category_scores_gemma":[0.05802765,0.001353745,0.0008477757,0.001961686,0.003282903,0.008463855,0.006342317,0.004195949,0.00226205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001174694,"about_ca_system_score_gemma":0.002057955,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000540379,"about_ca_topic_score_gemma":0.0008954736,"domain_scores_codex":[0.9835114,0.008104367,0.001954885,0.00179873,0.00346376,0.00116678],"domain_scores_gemma":[0.9257326,0.03071216,0.004227038,0.03428331,0.003996899,0.001048023],"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":[0.002241352,0.001046301,0.005119753,0.0006619723,0.000186975,0.0005324838,0.001621522,0.1854104,0.06401116,0.1452189,0.01367585,0.5802733],"study_design_scores_gemma":[0.0002696629,0.0005654267,0.0006668699,0.0001038553,0.00006120122,0.00031487,0.0002957101,0.8154373,0.07546873,0.0918135,0.01486268,0.0001402474],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04065061,0.0001980751,0.9436941,0.000290271,0.0000658508,0.0005688414,0.0001169956,0.01007884,0.004336494],"genre_scores_gemma":[0.4064719,0.0001561248,0.5868118,0.0001970213,0.00007530271,0.001412772,0.0004665489,0.002480485,0.00192808],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01352368,"threshold_uncertainty_score":0.07152092,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04662657331903598,"score_gpt":0.3256492104166988,"score_spread":0.2790226370976628,"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."}}